Socio-Semantic Frameworks for Techno-Social Systems
Roth, Camille
Socio-­semantic systems involve agents who create and process knowledge, exchange information, and create ties between ideas in a distributed manner: webloggers, communities of scientists, software developers and wiki contributors are, among others, examples of such networks. The state‐of‐the‐art in this regard focuses on two main issues that are generally addressed in an independent manner: the description of content dynamics and the study of social network characteristics and evolution. This talk will review recent endeavors to merge both types of dynamics into co‐evolutionary frameworks, where social and semantic aspects are being jointly appraised. Case studies featuring socio‐semantic networks, socio‐semantic hypergraphs and automatic socio‐semantic history reconstruction will be notably discussed.
 So Camille Roth, social semantic frameworks for techno-social systems. Thank you. So what I'd like to do today is to provide you with an overview of where we are standing at in terms of understanding the empirical social semantic dynamics, in the case of social technical systems. So what I'm doing is first presenting the main field of research. I'll try to be presenting the main field of research that can inform us on this type of social semantic dynamics. And then I will be presenting the state of the art of precisely the understanding of some empirical works that have shed light on the social semantic dynamics in the last decade, or let's say a couple of decades. And then I'll be presenting the most recent developments that try to precisely address the social semantic dynamics in a joint way, that is understanding the social and the semantic dynamics altogether. So first of all, I try to provide a definition of what I understand by social technical systems, and it's going to be a little bit narrower than what we've been talking about today, or yesterday as well, because it's going to be especially about social technical systems that are relevant for social semantic dynamics. So essentially that's systems where you have agents who are creating knowledge, processing information, exchanging concepts, connecting concepts in a distributed manner, in a decentralized manner, in such a way that as a whole the system produces a kind of operation of social cognition that is distributed over the whole system. So that concerns communities and systems that have been flourishing in the last few years, including online communities essentially, but also systems which have been historically present and also studied over the past decades, like communities of scientists. With respect to the web bloggers and the Wikic contributors, or people interacting online in online forums, or people dealing with tagging systems, posting stuff on Facebook and social networks, we have a much wider base of observation, and this is also why this field experimentally, empirically, proposes new possibilities. So that's going to be the social technical systems I'll be dealing with, and also a specific understanding of social cognition here, which is a little bit different from what we understand in cognitive psychology usually, in the sense that in psychology social cognition is about understanding how individuals react in social contexts. While here social cognition will rather be a notion of what is being done, what is being achieved by a system of people who are all together in a distributed manner, processing information, and at this level, at the system level, doing a cognition operation. So there are essentially three main streams of research, most relevant here, that could inform us on this, and I'll start with the most theoretical, that is not necessarily directly implementable in empirical terms, which is social and applied epistemology with three main subtopics, and the first one being understanding social factors influencing individual knowledge, like for instance, where does authority come from, how does it come that we are believing this fact and not that fact, and does it depend on the properties of the person who expresses these facts, or is there a social setting that explains this authority phenomenon. That's one stream that is relevant here, another stream which is particularly relevant, which is related to the organization of cognitive labor, like how within a system, within a group, cognitive operations are being distributed across agents, and in this sense, you can also find a term of distributed cognition, that's been also applied to scientific communities, where you see the process of the whole scientific community producing scientific knowledge as a distributed cognition operation, where each individual member is actually working on a specific subtopic and as a whole, without necessarily knowing it, or at least without having a centralized control, does this cognition operation. And there's also the third stream which is pretty much unrelated here, which is the notion of collective knowledge, or at least as the less linked with us, which is how does some piece of knowledge become a collective knowledge, that is how does it come that some proposition P is known by everybody, and can we assert the notion of collective knowledge as equivalent to this kind of formal logical proposition that this is a collective knowledge, if and only if everybody knows. So that's about social and applied epistemology, and that's probably the least connected to empirical things, because in general, this is about case studies that are not necessarily being developed on a wide scale. The second field is cultural anthropology, and this is much more related to the social-seminic dynamics, because cultural anthropologists have been interested in general at the fact that there is some similarity in cultural systems, that there are some subgroups, some aggregates of people who are actually having the same beliefs, that they are sharing the same norms, and the question is how does it come that we have individual interaction processes, and that as a whole, there emerges some similarity. So in this case, they are considering culture as acquired information, and the question is what are the microprocesses that lead to this macro-observation that there is some sort of stability. And maybe I'll describe just two, let's say, iconic theories in that field, one which is probably well known to you, which is mimetics, which basically assumes that there are memes, that there are atomic items of knowledge, that they are being replicated from agent to agent through a Hi-Fi process, it's being just basically copied and pasted from the brain to another brain, and then assuming that there is some fitness and some representations, the most, the fittest representations are basically getting more, better propagated. So this has opened the way to trying to validate empirically this kind of social-seminic dynamics, and has also received a lot of critics from the anthropologists themselves, as they are not entirely convinced that there is such a thing as an atomic item of knowledge, and also that there is not necessarily a process of Hi-Fi replication. So in this case, the second iconic theory, which is perhaps a little bit less known, is one introduced by Dan Sperber in 1996 that was assuming the existence of cultural epidemiology in the sense that cultural items or cultural representations are being propagated through a re-transformation process. What I'm telling you now is being interpreted by each one of you in their own terms and being reformulated at this level. There are some things that are understood, some things that are understood differently, and then this kind of re-transformation process may not be compared with a Hi-Fi replication thing. So there is the notion that there is a cultural, the notion of cultural representation basically is understood as a set of varying sub-representations, and that representation is basically the set of all its variants. The typical example here being the tale of the little red riding hood that has several versions that are all differing a little bit, and essentially the cultural representation little red riding hood is the set of all its variants. And underlying this, there is the assumption that there are attractive cognitive attractors that favor the transformation of some representations into some other representations, and this is what should lead to cultural similarity. The main, let's say the bottom line here with these two theories is that empirically there have been a few realistic attempts to validate them using real data. So at the moment, even if very recently there have been some progress in this field, this cultural anthropological theory have remained relatively unvalidated at the level of precision that they are providing. And so the third stream of research that's relevant here is much more recent, is deemed by some as being social computing. This is basically not necessarily related to social semantic dynamics, but essentially the aim of this research field is to infer information from the dynamics of social systems from large data sets, kind of big data, and of war, that goes from very basic things such as this minimal understanding of social computing as social sensing of the status of a system at a certain time, as the, maybe the participical example here would be Google Flu Trans, where it's possible to predict or to say what the state of the system is before the information gets aggregated at the institutional level. So there are social sensing of the current status of the system. There's also the possibility of trying to predict what the social system will be doing. And essentially again, from large data sets, statistically you have this paper in 2010 that was working on Twitter data that showed that you could predict the revenue of a movie by looking at the discussions regarding the movie the day it was first shown on screen. And so then there is this predictive and devour that also touches to social computing. There's also more about it in terms of understanding the micro level human dynamics that are behind the states of the system, and for instance understanding human mobility at the local level from large data sets again, describing movements of people. You have this study by Gonzalez and colleagues in 2008 that was based on cell phone data showing where people move in general across the city and across the territory in order to reconstruct the low level behavior of individuals. And in a more, maybe cognitively more interesting way, you have also the possibility of describing collective decision processes. For instance, in this study that was based on Wikipedia, the question was to know what influences the voting behavior of agents, depending on the information they have. So the underlying question here was to observe the traces of the decisions of individuals regarding the election of an administrator on the Wikipedia, and to see if there was an open available to all process of decision, and the underlying question about the human processes was about knowing whether people vote according to what they see other people have voted, so understanding hurting behavior, and also to understand whether people vote for people that they think are more experienced than they are. In this case, it's also possible because on Wikipedia, anyone can see the status of the person they are voting for or against, and see whether this person has much more experience on the Wikipedia or other. The relationship with the political science question is quite clear, whether people are voting according to their neighbors, and people are voting according to the experience that they are assigning to the people they are voting for. And the answer on Wikipedia that was one of the achievements you can get with the social computing is that essentially there is no hurting behavior that people are not voting according to what they have seen in terms of previous votes, and also that there is a very strong impact of the perceived experience of people because of the cut of the screen. There are a lot of differences in terms of edits between the person who votes and the person who is being voted for, and essentially you have much more positive votes for people who have two or three orders of magnitude of edits more than you, and you vote less or more negatively for people who have two or three orders of magnitude below you. So essentially that's how far it can get in terms of social computing. You take these big data sets and then you ask questions to the data sets. It's pretty familiar, but that's the sort of field of research that's really relevant here for the social semantic dynamics. So coming back to the main question, what would be essentially needed for an empirical science based on this in vivo data of these social cognition processes? Essentially we can distinguish two main questions. It's the question of knowing the shape of the interactions, that is the substrate onto which this social cognition occurs, and the second question is knowing after that the dynamics of social transmission, information transmission, the dynamics of contents that occurs on this substrate. And also being able, and that's a very important question here, to be able to describe those cultural items, connecting with the cultural anthropology critics that are pointing towards the non-existence of atomic items and so on and so forth. So there are these two questions. So where do we stand at? In terms of social network, I won't spend too much time on that because I guess everybody is familiar with that. So just a very quick historical hindsight here to see that in fact there is a big difference in terms of advancement between the knowledge of the social shape of interactions and the diffusion of content. There has been a first period of development that actually goes back very early in time in the 40s, and let's say up to the 70s it's been essentially populated by social scientists who were doing mathematical sociology. They were working on small case studies, basically from data that they collected in the field. So essentially 20s, 30s, 40 people maximum, with very qualitative empirical data sets describing the relationships within a certain social group. But still most of the definitions that we are using today are based on this effort of formalization in the case of small networks, essentially centrality, local patterns, betweenness, or even tried to close the notion of communities, were also the focus of these communities of scientists. So that was the first step and then the second period that we know much better. Here is what's been called sometimes the new science of networks that has changed a little bit the focus, moving towards large scale data sets, having a complex system standpoint as you all know, and redeveloping in a much more formal and quantitative way these initial insights on power-lows, communities, also introducing morphogenesis models, which were also present in the 40s to the 70s, but introducing stochastic behavior and agent-based models in a pretty new way. There's just an illustration here of the type of indiverse that we see now, which have a little bit shifted from the very beginning of this new science of networks, where initially we've been looking for universal patterns in those networks, and right now I would say the trend is more to look for the individual differences between specific networks and specific contexts. In this case, for instance, in this very example, in this very paper, we've been looking at the cascade dynamics in the blog network, looking at specific patterns that are really defined at the level of a blog network, for people diffusing content, citing somebody who also diffuse this content that provides these kind of small cascading patterns. And then the question is, given these kind of patterns that are really specific, try to find a morphogenesis model that is also very specific. In this case, you have an agent-based model, very recent, that starts with a random work process that makes it possible to generate posts, and then the posts are possibly linking or not linking to other posts, choosing or not choosing a neighbor, a previous neighbor, and then possibly choosing a post of this non-neighbor or neighbor, and then and so on and so forth. And eventually, these kind of very adapted morphogenesis models make it possible to recreate the distribution of patterns that have been seen initially. So just this quick overview to basically say that in terms of social network, we've got a pretty good knowledge of what happens. We've got two points where we have very sophisticated models in very specific cases. It's less clear that we have the same kind of knowledge and the dynamics of content. There's been a recent work on dynamics of conversations, for instance, online conversations, that also were only possibly observable very recently as well, so it's almost atological to say that this effort is pretty recent. So in this paper, there's just a very first attempt, but it's 2004, to define some simple dynamic features of the usage of a certain term. So it's really basic because it's at the level of a term, and it's been so far several years after that, and it's just trying to create a dichotomy between terms that are chattering or terms that are spiking. So really essentially basic, but then it's been extended further by looking at the dynamics of term usage, differentiating between the source types, in this case the question being, are there different usages between the news, the mainstream news and the blogs, and can we find temporal correlations in the usage of some term, or versus the location when we have geographic data, and these dynamics of term usage can be described in terms of specific locations that use specific terms, so eventually you can imagine some evolutionary linguistics that is also geographically grounded. In this case you see the usage of the variant spelling of hello or hellover, hellover something that occurs in different areas in the US. So going further into the differentiation, there's also pretty much in the line of social computing and divorce being some works that try to predict the usage of terms. That is, now that the system is dealing with some issues, can we predict how it will deal with some issues in the future? So in this case what you have is, for instance, the variations in the usage of drunk that is pretty much regular, you notice that there's a first peak on Friday morning and there's a second peak on Saturday morning and then the highest peak is on Sunday morning. What is most interesting here is that it happens every week. It's probably not a very surprising observation, but it shows that even at this very simple level you can already start to get some prediction about what humans will do next week or at which time. So you also have variations and divergences that are also interesting in this case. For instance, if you look at much less precise terms such as sad and busy, you see models that learn from the data to predict the actual usage from a week to another week. So the prediction is actually the dashed line and the actual observation is the solid line. We see in these two graphs that the prediction works pretty well and actually works every week as well, except for this case of July 7, 2007, that corresponds to English-speaking blogs in the UK on the day of the terrorist attacks. So essentially, in this case, divergence corresponds precisely to a change in the underlying state of the system. So that's also indicative of this kind of variations. So that's the dynamics of terms, but there have been also works that have been trying to extend a little bit this viewpoint because the term is a very simplistic thing and of course, in the cultural anthropologists would not even look at it. So they've been trying to see the dynamics of sentences, finding, for instance, quotations in blog networks and precisely describing how the quotations may vary. The intuition is that the quotation, the quotation of a political figure, for instance, should be the same from a blog to another blog or from a source to another source. But it is the case that even at this level of something that should undergo high-five replication, there is already a lot of variations. So essentially, they gathered all quotations that were similar in terms of edit distance and some of the features in order to group these sentences in clusters that could be pretty much corresponding to this general idea of sparbar, of a cultural representation being the set of all its variants. And so essentially, they've been at this level looking at the dynamics of these concepts at this semantic mezzol level. You also have works on the dynamics of disciplines. Maybe I skip that because it's not necessarily the most important thing at this time. What I want to move to now is to see how this is possible, how could we actually connect the structure and the content. This is the most recent endeavor, trying to empirically describe how opinions or expressions are being present differently in terms of the structure and how they are being diffused differently as well. So in this case, you have a very binary thing, which is the blue are the Democrats and the red are the Republicans. And in this case, this is the US blogosphere in the 2004 that describes the links between the two, between blogs actually in this blogosphere and showing that essentially there are two structurally disconnected communities. That's, in this case, in terms of content, extremely simplistic because we have only a binary indicator, which is Democrat or Republican, but it makes it possible to already combine the social network view with the content that is being discussed on it. Coming back to this discussion, diffusion cascades with these patterns, it is also possible when you look at, again, a blog network to classify a given blog or a given subset of blogs according to the distribution of cascades that you observe in these blogs. The observation in that paper was that when you do an unsupervised categorization of the distribution of patterns in this specific network, you see differences between typical communities in the sense that you can discriminate pretty well. For instance, blogs that are discussing conservative content and blogs that are discussing humoristic content. So in this case, that is quite far from universal observations on the systems because at this level, we even see some dimensions of differentiation between blogs discussing a specific topic and blogs discussing another topic. So that is a conclusion on the social semantic morphology in this case, that there is no universal social morphology in the blog networks, but that it is differentiated semantically as well. To come to a study that we did recently, 2009-2010, where we've been observing the US blogosphere during the presidential primaries, where we've been basically looking at the co-evolution between the social network and the semantic content, the question was to understand whether there was a specific distribution of content that depended on the social network empirically and in the reverse direction whether there was dynamics of the social network that depended on the distribution of content. So the two dynamics jointly co-evolving together. Is it possible to appraise these kind of features? So one of the first observations is that basically, statically, you have a very strong homophily. If you look at blogs in terms of the terms that they are using and in terms of the n-grams that they are using, let's say climate change, taxation reduction, these kind of terms, you can describe blogs as sets of topics that they are addressing. And so you can also describe the semantic dissimilarity that you have between some blogs and some other. And in this case, you see that if you look at the blue curve in terms of the semantic dissimilarity, which goes from zero for blogs addressing exactly the same topics to one for blogs addressing strictly different topics, you have essentially between the connected blogs a much closer semantic similarity. So this is homophily-aposteriority. You observe some sort of homophily-aposteriority once the links have been there. And so the question can be connected to this general cultural anthropologic question, which is where does dissimilarity come from? Or do we have, again, empirical means to observe this in vivo and to try to understand what are the general dynamics of this system? So you can look at the social network dynamics in terms of the social network itself, for instance, looking at the prevention for interaction to occur depending on the distance within the networks, observing that, for instance, you have essentially a very high prevention for repeated interaction at distance one between connected blogs, and then it exponentially decreases up to a certain point where the preference of interaction, the preference, basically, of the social network formation with respect to the social network is not influenced anymore by the distance. In other words, beyond a certain horizon, there is no more influence of the distance, and so the fact that you are at distance five or six of any possible other blogs doesn't change anything. We might speak of a narrow world where all interactions are happening in a narrow circle rather than a small world where all interactions are theoretically possible with any blog that is generally not farther than distance six. So what is more interesting is to combine this social network dynamics with respect to the content and to see, so this is, again, a prevention of interaction that describes how likely you are to interact with some nodes that have some properties or some couple of properties. And when you look at the differentiation between the social distance and the semantic distance, what you see in these 3D graphs is that basically, if you look at these blogs that are far apart, it's like distance three and more, you have a strict monotonic decrease of the prevention of interaction. It is in the farther world, your prevention of interaction essentially depends on the semantic distance. So there is an a priori homophily that you will tend to interact with blogs that are similar, but when you go closer to the closed network that is a distance one, what you see is that essentially this effect doesn't hold anymore. The distance one within the repeated interaction, you can have the same prevention to interact with individual that are also dissimilar. And essentially what is not shown here is that you have also preference for higher degree nodes that is also depending on the semantic distance as well. In the sense that you tend to interact with higher degree nodes when they are essentially dissimilar, but when they are similar, the degree does not influence the interaction. And what is also present here is that there is a contraction of the network after interaction that describes the dynamics of the social network once people have interacted that shows that there is a contraction in terms of the semantic dissimilarity. Once you interact with another blog, you tend to be more similar, but what is interesting is that this phenomenon occurs before interaction as well. Zero is the time where interaction occurs and this is the semantic similarity that shows that about three weeks before the given blogs are going to interact or are going to side each other, they are already starting to get closer in terms of content. So the same happens in the social network in the sense that when you interact, there is a local contraction of the network that also could be observed before the interaction. So that describes just in a nutshell and just to be quick on this, that describes how the social network is evolving depending on these two dimensions, the social network itself and the semantic content. And now we can look at the effect of the social network and diffusion, again with in vivo data. So looking at cascades that are, as I said, a blog saying something and then being cited by other blogs that say the same thing later in the past, where we say that if this kind of pattern occurs, it is likely to be an explicit proof evidence of explicit diffusion. So what we see is that it depends, let's not spend too much time on describing the variables, but basically you have the total attention, which is the amount of attention the blog gets in terms of the share they relatively get from the other bloggers. So it's not the degree, it's really the share of the links that come to you with respect to the number of links that the person that points to you is also distributing to the rest of the system. So for instance, if you have two links and you have one link to one person, you give one half of attention to this person. So you can have aggregated this, what it shows is that at this very local level, the diffusion is influenced by this intention process in a nonlinear way, in the sense that it plateaus, and also it is not growing as fast at the very beginning. So basically for blogs that have little to very little attention, there is the same type of diffusion impact and the same for blogs that have strong to very strong attention, but then there's an intermediate zone where this impact is maximum. More interestingly, we observe this kind of diffusion processes, content diffusion with respect to the social network at a more mezzal level, where we look at the influence of being a bridge between communities, and in this case empirically we see, according to this measure, which you may interpret in terms of being a weak link or not, that weak links are maximizing or correspond to a maximum number of transmissions. So basically being connecting two remote, but not two remote communities is of importance. This kind of debunking the notion that a weak link may always be favoring diffusion because after a certain level there is no more influence of that. Maybe to finish with the two minutes that I have, going a little bit further on the social semantic influence effects, there's this study by Shia and colleagues two years ago on Twitter, where they've been looking at, again, this influence effects in terms of retweeting somebody or mentioning somebody, or also simply having some authority, which is measured by the indegree that the Twitter blogger has, trying to understand how these different dimensions correlate. Is there a correlation between the indegree that you have, the incoming links, and the number of times you get retweeted, or the number of times you get mentioned, which are also two different influence effects. And so what they found basically is that there is no observable correlation between a high-degree node and the number of retweets. So this Venn diagram, for instance, shows that if you take the top 100 influentials, in terms of retweets, mentions an indegree, there is little overlap between those top influentials. But essentially, apart from these structural effects on the diffusion, you also have a semantic effect on the diffusion, which means that if you look at the Twitter blogger, and if you look at the set of topics that they address, you see that there is a boost of influence for Twitter users who are basically narrowing their discourse on a small set of topics, essentially, except for a few exceptions. So what this means is that there is essentially an impact of the semantic properties of the originator on the influence, and not only social network features. But this is also one way to appraise this kind of social-seminic dynamics that are not strictly social and not strictly separable, one from the other. And I won't... Let's say we'll skip to the end. That is, that essentially, apart from this overview, what we are seeing now is that we are going towards an experimental science of social-seminic dynamics that is possible with the social-technical systems, and while we have quite some good knowledge on the social network features, we are really relatively blind, again empirically, regarding the local cognition processes, how this information is being transformed from a user to the other, or even just the ontology of what we are talking about. What are we talking about? Are we talking about terms being diffused? Are we talking about sentences? Are we talking about notions? There is a lot of work to do here. And so I'll be welcoming questions. Thanks for your attention so far. Thank you very much. APPLAUSE Great talk. Are there questions? Okay, then let me ask a question. So one interesting phenomenon I can see is, and I think it's an interesting discussion to have in an agent-based modeling conference, right? So digital humanities is not as universal as it says it is, because it's basically 90% linguists. And I have the same feeling for the semantic web community right now. It's actually 90% sociologists. So if we talk about, and this is actually an interesting mixture, right? So there's the semantic and linguist people who know how to datamine semantic words and whatever, and there is the social guys who datamine people. And the interesting thing is that there is differences in the perspective, not only in terms of using real data and whatever, but so for example, you called power laws stylized facts, which a physicist would call not a universal law, but something he would look at as a universal law, while you were looking at regularities, like the daily drunk pattern, or the weekly drunk pattern, and you called that predictive, which the physicists would deny, right? Because you would say, okay, patterns you can find everywhere, datamining is only taking you somewhere. So one thing I also would like to throw in the audience is, is basically what are the things to be gained by actually abolishing the prejudices against each other, and actually what are the key ingredients. So in addition to what you have shown, what's all valid, it's all nice, are we missing something? Is there beyond cultural anthropology, beyond data miners and physicists, what are the fields we actually would need to do this and take it to the next level? You mean the social science field, right? The social semantic kind of field, where we have linked data of any kind, right? Which is basically what it is, in a sense. Yeah, well, what you can basically say is that there is actually no, for the moment, there is no widespread knowledge of this big data thing, or even the modelling and evores in the social science communities. So essentially I would say that you'd need to attract, or even like to get knowledge of the communities that are relevant here, you would necessarily have to start a pedagogical effort towards the few people who might be at the interface, because there are also many communities who are not interested into these high level, or large scale characterizations. So essentially you have very few communities, for instance in anthropology, that would be ready to listen to this kind of discourse. Except for the specific people who have been working on this naturalization, of cultural dynamics, or social semantic dynamics, and who are ready to introduce models into their field. And they are actually doing that for the moment. But it would be very difficult to map the communities that are relevant in the social science fields, because they are, for the moment, restricted to individuals, rather than sub-fields, actually, breast-pings. So, that's a pretty good question. Your data are related to social networks, like Twitter or so, but you mentioned also scientific systems, and what is missing, I feel, is relation of power. Do you think that this relation can be built into by connectivity? What is missing, sorry, I didn't understand. It's relations of power. Relations of power. Well, that's part of the authority proxy, in some way, that you are looking at. For the moment it's kind of a superficial outlook, because it really tries to describe the main determinants of authority, or influence effects, that could be at least quantitatively at a statistical level, described by simple structural features. The notion of relations of power, in the case of scientific communities, that would be really relevant, because then you could introduce parameters that relate to this. In the case of online communities, it is also relevant, but it's often outside of the observational scope, because that, for instance, the credibility of an individual, and the outside credibility of an individual, is difficult to describe, to deduce from these traces. So that's also, I think, it touches to what are the questions, that we can ask, given the data that we have. And in this case, the power relationship, I think they are beyond the general research program that is here, at least for the moment. Okay, one last question. By the way, if anybody has the other microphone, please. Well, I'm Tartan, come from University College London. I found this talk is very interesting, maybe my, because this area is relatively new for me. My question is really, what's real, you can think of the potential application of understanding this social semantics. I give the example you gave about the drunk. I think it's very kind of, kind of an obvious example, because drinking behavior, I mean, we normally say in the wake-end, people do drink more than normal, so we can say not one can be useful for prediction. But if you bring that example to this broad term of social semantics, so what do you think would be possible, as you said, in the point here, the first one, we lowered good knowledge about the morphology power law thing. But how are we going to move to the next stage? I'm going to make a useful thought for that. Yeah, I'll start with, I guess, a very qualitative question, which is one that is, for instance, let's stay in the web, and there is a typical political science and also sociology question now, which is, are the online interactions creating a vulcanized world where everybody goes towards content that they are agreeing with, that they are familiar with, and that with the internet, we are witnessing the emergence of isolated semantic communities. So, is it the case that would be one of the first possibilities of this kind of studies, how could it be avoided in some way, or weakened, if this is what we are looking for? And so that would open to normative models where we'd like to favor some kinds of behaviors or connections that would mitigate this kind of effect. That could be one of the questions here. You'd have, let's say, a political blogosphere that is pretty well segmented with people who are getting fed with content or opinions that they are agreeing with, and so what is the structure of these communities and are there some discussion paths that are possible and what is the implication on the political debate in general, especially if most of the political debate or a large part of the political debate moves to the internet.
 So Camille Roth, social semantic frameworks for techno-social systems.  Thank you.  So what I'd like to do today is to provide you with an overview of where we are standing at in terms of understanding the empirical social semantic dynamics, in the case of social technical systems.  So what I'm doing is first presenting the main field of research.  I'll try to be presenting the main field of research that can inform us on this type of social semantic dynamics.  And then I will be presenting the state of the art of precisely the understanding of some empirical works that have shed light on the social semantic dynamics in the last decade, or let's say a couple of decades.  And then I'll be presenting the most recent developments that try to precisely address the social semantic dynamics in a joint way, that is understanding the social and the semantic dynamics altogether.  So first of all, I try to provide a definition of what I understand by social technical systems, and it's going to be a little bit narrower than what we've been talking about today, or yesterday as well,  because it's going to be especially about social technical systems that are relevant for social semantic dynamics.  So essentially that's systems where you have agents who are creating knowledge, processing information, exchanging concepts, connecting concepts in a distributed manner, in a decentralized manner,  in such a way that as a whole the system produces a kind of operation of social cognition that is distributed over the whole system.  So that concerns communities and systems that have been flourishing in the last few years, including online communities essentially, but also systems which have been historically present and also studied over the past decades,  like communities of scientists.  With respect to the web bloggers and the Wikic contributors, or people interacting online in online forums, or people dealing with tagging systems, posting stuff on Facebook and social networks, we have a much wider base of observation,  and this is also why this field experimentally, empirically, proposes new possibilities.  So that's going to be the social technical systems I'll be dealing with, and also a specific understanding of social cognition here, which is a little bit different from what we understand in cognitive psychology usually,  in the sense that in psychology social cognition is about understanding how individuals react in social contexts.  While here social cognition will rather be a notion of what is being done, what is being achieved by a system of people who are all together in a distributed manner, processing information,  and at this level, at the system level, doing a cognition operation.  So there are essentially three main streams of research, most relevant here, that could inform us on this, and I'll start with the most theoretical, that is not necessarily directly implementable in empirical terms,  which is social and applied epistemology with three main subtopics, and the first one being understanding social factors influencing individual knowledge,  like for instance, where does authority come from, how does it come that we are believing this fact and not that fact,  and does it depend on the properties of the person who expresses these facts, or is there a social setting that explains this authority phenomenon.  That's one stream that is relevant here, another stream which is particularly relevant, which is related to the organization of cognitive labor,  like how within a system, within a group, cognitive operations are being distributed across agents,  and in this sense, you can also find a term of distributed cognition, that's been also applied to scientific communities,  where you see the process of the whole scientific community producing scientific knowledge as a distributed cognition operation,  where each individual member is actually working on a specific subtopic and as a whole, without necessarily knowing it, or at least without having a centralized control,  does this cognition operation. And there's also the third stream which is pretty much unrelated here, which is the notion of collective knowledge,  or at least as the less linked with us, which is how does some piece of knowledge become a collective knowledge, that is how does it come that some proposition P is known by everybody,  and can we assert the notion of collective knowledge as equivalent to this kind of formal logical proposition that this is a collective knowledge,  if and only if everybody knows. So that's about social and applied epistemology, and that's probably the least connected to empirical things,  because in general, this is about case studies that are not necessarily being developed on a wide scale.  The second field is cultural anthropology, and this is much more related to the social-seminic dynamics,  because cultural anthropologists have been interested in general at the fact that there is some similarity in cultural systems,  that there are some subgroups, some aggregates of people who are actually having the same beliefs, that they are sharing the same norms,  and the question is how does it come that we have individual interaction processes, and that as a whole, there emerges some similarity.  So in this case, they are considering culture as acquired information, and the question is what are the microprocesses that lead to this macro-observation  that there is some sort of stability. And maybe I'll describe just two, let's say, iconic theories in that field,  one which is probably well known to you, which is mimetics, which basically assumes that there are memes,  that there are atomic items of knowledge, that they are being replicated from agent to agent through a Hi-Fi process,  it's being just basically copied and pasted from the brain to another brain, and then assuming that there is some fitness and some representations,  the most, the fittest representations are basically getting more, better propagated.  So this has opened the way to trying to validate empirically this kind of social-seminic dynamics,  and has also received a lot of critics from the anthropologists themselves, as they are not entirely convinced that there is such a thing as an atomic item of knowledge,  and also that there is not necessarily a process of Hi-Fi replication.  So in this case, the second iconic theory, which is perhaps a little bit less known, is one introduced by Dan Sperber in 1996  that was assuming the existence of cultural epidemiology in the sense that cultural items or cultural representations are being propagated through a re-transformation process.  What I'm telling you now is being interpreted by each one of you in their own terms and being reformulated at this level.  There are some things that are understood, some things that are understood differently,  and then this kind of re-transformation process may not be compared with a Hi-Fi replication thing.  So there is the notion that there is a cultural, the notion of cultural representation basically is understood as a set of varying sub-representations,  and that representation is basically the set of all its variants.  The typical example here being the tale of the little red riding hood that has several versions that are all differing a little bit,  and essentially the cultural representation little red riding hood is the set of all its variants.  And underlying this, there is the assumption that there are attractive cognitive attractors that favor the transformation of some representations into some other representations,  and this is what should lead to cultural similarity.  The main, let's say the bottom line here with these two theories is that empirically there have been a few realistic attempts to validate them using real data.  So at the moment, even if very recently there have been some progress in this field,  this cultural anthropological theory have remained relatively unvalidated at the level of precision that they are providing.  And so the third stream of research that's relevant here is much more recent, is deemed by some as being social computing.  This is basically not necessarily related to social semantic dynamics, but essentially the aim of this research field is to infer information  from the dynamics of social systems from large data sets, kind of big data, and of war, that goes from very basic things such as this minimal understanding of social computing  as social sensing of the status of a system at a certain time, as the, maybe the participical example here would be Google Flu Trans,  where it's possible to predict or to say what the state of the system is before the information gets aggregated at the institutional level.  So there are social sensing of the current status of the system.  There's also the possibility of trying to predict what the social system will be doing.  And essentially again, from large data sets, statistically you have this paper in 2010 that was working on Twitter data that showed that you could predict  the revenue of a movie by looking at the discussions regarding the movie the day it was first shown on screen.  And so then there is this predictive and devour that also touches to social computing.  There's also more about it in terms of understanding the micro level human dynamics that are behind the states of the system,  and for instance understanding human mobility at the local level from large data sets again, describing movements of people.  You have this study by Gonzalez and colleagues in 2008 that was based on cell phone data showing where people move in general across the city  and across the territory in order to reconstruct the low level behavior of individuals.  And in a more, maybe cognitively more interesting way, you have also the possibility of describing collective decision processes.  For instance, in this study that was based on Wikipedia, the question was to know what influences the voting behavior of agents,  depending on the information they have.  So the underlying question here was to observe the traces of the decisions of individuals regarding the election of an administrator on the Wikipedia,  and to see if there was an open available to all process of decision, and the underlying question about the human processes was about knowing whether people vote  according to what they see other people have voted, so understanding hurting behavior,  and also to understand whether people vote for people that they think are more experienced than they are.  In this case, it's also possible because on Wikipedia, anyone can see the status of the person they are voting for or against,  and see whether this person has much more experience on the Wikipedia or other.  The relationship with the political science question is quite clear,  whether people are voting according to their neighbors, and people are voting according to the experience that they are assigning to the people they are voting for.  And the answer on Wikipedia that was one of the achievements you can get with the social computing is that essentially there is no hurting behavior  that people are not voting according to what they have seen in terms of previous votes,  and also that there is a very strong impact of the perceived experience of people because of the cut of the screen.  There are a lot of differences in terms of edits between the person who votes and the person who is being voted for,  and essentially you have much more positive votes for people who have two or three orders of magnitude of edits more than you,  and you vote less or more negatively for people who have two or three orders of magnitude below you.  So essentially that's how far it can get in terms of social computing.  You take these big data sets and then you ask questions to the data sets.  It's pretty familiar, but that's the sort of field of research that's really relevant here for the social semantic dynamics.  So coming back to the main question, what would be essentially needed for an empirical science based on this in vivo data of these social cognition processes?  Essentially we can distinguish two main questions.  It's the question of knowing the shape of the interactions, that is the substrate onto which this social cognition occurs,  and the second question is knowing after that the dynamics of social transmission, information transmission,  the dynamics of contents that occurs on this substrate.  And also being able, and that's a very important question here, to be able to describe those cultural items, connecting with the cultural anthropology critics  that are pointing towards the non-existence of atomic items and so on and so forth.  So there are these two questions.  So where do we stand at?  In terms of social network, I won't spend too much time on that because I guess everybody is familiar with that.  So just a very quick historical hindsight here to see that in fact there is a big difference in terms of advancement  between the knowledge of the social shape of interactions and the diffusion of content.  There has been a first period of development that actually goes back very early in time in the 40s,  and let's say up to the 70s it's been essentially populated by social scientists who were doing mathematical sociology.  They were working on small case studies, basically from data that they collected in the field.  So essentially 20s, 30s, 40 people maximum, with very qualitative empirical data sets describing the relationships within a certain social group.  But still most of the definitions that we are using today are based on this effort of formalization in the case of small networks,  essentially centrality, local patterns, betweenness, or even tried to close the notion of communities,  were also the focus of these communities of scientists.  So that was the first step and then the second period that we know much better.  Here is what's been called sometimes the new science of networks that has changed a little bit the focus,  moving towards large scale data sets, having a complex system standpoint as you all know,  and redeveloping in a much more formal and quantitative way these initial insights on power-lows, communities,  also introducing morphogenesis models, which were also present in the 40s to the 70s,  but introducing stochastic behavior and agent-based models in a pretty new way.  There's just an illustration here of the type of indiverse that we see now,  which have a little bit shifted from the very beginning of this new science of networks,  where initially we've been looking for universal patterns in those networks,  and right now I would say the trend is more to look for the individual differences between specific networks and specific contexts.  In this case, for instance, in this very example, in this very paper, we've been looking at the cascade dynamics  in the blog network, looking at specific patterns that are really defined at the level of a blog network,  for people diffusing content, citing somebody who also diffuse this content  that provides these kind of small cascading patterns.  And then the question is, given these kind of patterns that are really specific,  try to find a morphogenesis model that is also very specific.  In this case, you have an agent-based model, very recent, that starts with a random work process  that makes it possible to generate posts, and then the posts are possibly linking or not linking to other posts,  choosing or not choosing a neighbor, a previous neighbor,  and then possibly choosing a post of this non-neighbor or neighbor, and then and so on and so forth.  And eventually, these kind of very adapted morphogenesis models  make it possible to recreate the distribution of patterns that have been seen initially.  So just this quick overview to basically say that in terms of social network,  we've got a pretty good knowledge of what happens.  We've got two points where we have very sophisticated models in very specific cases.  It's less clear that we have the same kind of knowledge and the dynamics of content.  There's been a recent work on dynamics of conversations, for instance, online conversations,  that also were only possibly observable very recently as well,  so it's almost atological to say that this effort is pretty recent.  So in this paper, there's just a very first attempt, but it's 2004,  to define some simple dynamic features of the usage of a certain term.  So it's really basic because it's at the level of a term,  and it's been so far several years after that,  and it's just trying to create a dichotomy between terms that are chattering or terms that are spiking.  So really essentially basic, but then it's been extended further  by looking at the dynamics of term usage, differentiating between the source types,  in this case the question being, are there different usages between the news,  the mainstream news and the blogs, and can we find temporal correlations  in the usage of some term, or versus the location when we have geographic data,  and these dynamics of term usage can be described in terms of specific locations  that use specific terms, so eventually you can imagine some evolutionary linguistics  that is also geographically grounded.  In this case you see the usage of the variant spelling of hello or hellover,  hellover something that occurs in different areas in the US.  So going further into the differentiation, there's also pretty much in the line of social computing  and divorce being some works that try to predict the usage of terms.  That is, now that the system is dealing with some issues,  can we predict how it will deal with some issues in the future?  So in this case what you have is, for instance, the variations in the usage of drunk  that is pretty much regular, you notice that there's a first peak on Friday morning  and there's a second peak on Saturday morning and then the highest peak is on Sunday morning.  What is most interesting here is that it happens every week.  It's probably not a very surprising observation, but it shows that even at this very simple level  you can already start to get some prediction about what humans will do next week  or at which time.  So you also have variations and divergences that are also interesting in this case.  For instance, if you look at much less precise terms such as sad and busy,  you see models that learn from the data to predict the actual usage from a week to another week.  So the prediction is actually the dashed line and the actual observation is the solid line.  We see in these two graphs that the prediction works pretty well  and actually works every week as well, except for this case of July 7, 2007,  that corresponds to English-speaking blogs in the UK on the day of the terrorist attacks.  So essentially, in this case, divergence corresponds precisely to a change  in the underlying state of the system.  So that's also indicative of this kind of variations.  So that's the dynamics of terms, but there have been also works that have been trying to  extend a little bit this viewpoint because the term is a very simplistic thing  and of course, in the cultural anthropologists would not even look at it.  So they've been trying to see the dynamics of sentences, finding, for instance,  quotations in blog networks and precisely describing how the quotations may vary.  The intuition is that the quotation, the quotation of a political figure, for instance,  should be the same from a blog to another blog or from a source to another source.  But it is the case that even at this level of something that should undergo high-five replication,  there is already a lot of variations.  So essentially, they gathered all quotations that were similar in terms of edit distance  and some of the features in order to group these sentences in clusters  that could be pretty much corresponding to this general idea of sparbar,  of a cultural representation being the set of all its variants.  And so essentially, they've been at this level looking at the dynamics of these concepts  at this semantic mezzol level.  You also have works on the dynamics of disciplines.  Maybe I skip that because it's not necessarily the most important thing at this time.  What I want to move to now is to see how this is possible,  how could we actually connect the structure and the content.  This is the most recent endeavor, trying to empirically describe how opinions or expressions  are being present differently in terms of the structure  and how they are being diffused differently as well.  So in this case, you have a very binary thing, which is the blue are the Democrats  and the red are the Republicans.  And in this case, this is the US blogosphere in the 2004 that describes the links between the two,  between blogs actually in this blogosphere and showing that essentially  there are two structurally disconnected communities.  That's, in this case, in terms of content, extremely simplistic  because we have only a binary indicator, which is Democrat or Republican,  but it makes it possible to already combine the social network view with the content  that is being discussed on it.  Coming back to this discussion, diffusion cascades with these patterns,  it is also possible when you look at, again, a blog network  to classify a given blog or a given subset of blogs  according to the distribution of cascades that you observe in these blogs.  The observation in that paper was that when you do an unsupervised categorization  of the distribution of patterns in this specific network,  you see differences between typical communities in the sense that you can discriminate pretty well.  For instance, blogs that are discussing conservative content  and blogs that are discussing humoristic content.  So in this case, that is quite far from universal observations on the systems  because at this level, we even see some dimensions of differentiation  between blogs discussing a specific topic and blogs discussing another topic.  So that is a conclusion on the social semantic morphology in this case,  that there is no universal social morphology in the blog networks,  but that it is differentiated semantically as well.  To come to a study that we did recently, 2009-2010,  where we've been observing the US blogosphere during the presidential primaries,  where we've been basically looking at the co-evolution between the social network and the semantic content,  the question was to understand whether there was a specific distribution of content  that depended on the social network empirically and in the reverse direction  whether there was dynamics of the social network that depended on the distribution of content.  So the two dynamics jointly co-evolving together.  Is it possible to appraise these kind of features?  So one of the first observations is that basically, statically, you have a very strong homophily.  If you look at blogs in terms of the terms that they are using  and in terms of the n-grams that they are using, let's say climate change,  taxation reduction, these kind of terms, you can describe blogs as sets of topics that they are addressing.  And so you can also describe the semantic dissimilarity that you have between some blogs and some other.  And in this case, you see that if you look at the blue curve in terms of the semantic dissimilarity,  which goes from zero for blogs addressing exactly the same topics to one for blogs addressing strictly different topics,  you have essentially between the connected blogs a much closer semantic similarity.  So this is homophily-aposteriority.  You observe some sort of homophily-aposteriority once the links have been there.  And so the question can be connected to this general cultural anthropologic question,  which is where does dissimilarity come from?  Or do we have, again, empirical means to observe this in vivo  and to try to understand what are the general dynamics of this system?  So you can look at the social network dynamics in terms of the social network itself,  for instance, looking at the prevention for interaction to occur depending on the distance within the networks,  observing that, for instance, you have essentially a very high prevention for repeated interaction  at distance one between connected blogs, and then it exponentially decreases up to a certain point  where the preference of interaction, the preference, basically, of the social network formation  with respect to the social network is not influenced anymore by the distance.  In other words, beyond a certain horizon, there is no more influence of the distance,  and so the fact that you are at distance five or six of any possible other blogs doesn't change anything.  We might speak of a narrow world where all interactions are happening in a narrow circle  rather than a small world where all interactions are theoretically possible  with any blog that is generally not farther than distance six.  So what is more interesting is to combine this social network dynamics with respect to the content  and to see, so this is, again, a prevention of interaction that describes how likely you are  to interact with some nodes that have some properties or some couple of properties.  And when you look at the differentiation between the social distance and the semantic distance,  what you see in these 3D graphs is that basically, if you look at these blogs that are far apart,  it's like distance three and more, you have a strict monotonic decrease of the prevention of interaction.  It is in the farther world, your prevention of interaction essentially depends on the semantic distance.  So there is an a priori homophily that you will tend to interact with blogs that are similar,  but when you go closer to the closed network that is a distance one,  what you see is that essentially this effect doesn't hold anymore.  The distance one within the repeated interaction, you can have the same prevention to interact with individual  that are also dissimilar.  And essentially what is not shown here is that you have also preference for higher degree nodes  that is also depending on the semantic distance as well.  In the sense that you tend to interact with higher degree nodes when they are essentially dissimilar,  but when they are similar, the degree does not influence the interaction.  And what is also present here is that there is a contraction of the network after interaction  that describes the dynamics of the social network once people have interacted  that shows that there is a contraction in terms of the semantic dissimilarity.  Once you interact with another blog, you tend to be more similar,  but what is interesting is that this phenomenon occurs before interaction as well.  Zero is the time where interaction occurs and this is the semantic similarity  that shows that about three weeks before the given blogs are going to interact or are going to side each other,  they are already starting to get closer in terms of content.  So the same happens in the social network in the sense that when you interact,  there is a local contraction of the network that also could be observed before the interaction.  So that describes just in a nutshell and just to be quick on this,  that describes how the social network is evolving depending on these two dimensions,  the social network itself and the semantic content.  And now we can look at the effect of the social network and diffusion, again with in vivo data.  So looking at cascades that are, as I said, a blog saying something  and then being cited by other blogs that say the same thing later in the past,  where we say that if this kind of pattern occurs, it is likely to be an explicit proof evidence of explicit diffusion.  So what we see is that it depends, let's not spend too much time on describing the variables,  but basically you have the total attention, which is the amount of attention the blog gets  in terms of the share they relatively get from the other bloggers.  So it's not the degree, it's really the share of the links that come to you  with respect to the number of links that the person that points to you  is also distributing to the rest of the system.  So for instance, if you have two links and you have one link to one person,  you give one half of attention to this person.  So you can have aggregated this, what it shows is that at this very local level,  the diffusion is influenced by this intention process in a nonlinear way,  in the sense that it plateaus, and also it is not growing as fast at the very beginning.  So basically for blogs that have little to very little attention,  there is the same type of diffusion impact and the same for blogs that have strong to very strong attention,  but then there's an intermediate zone where this impact is maximum.  More interestingly, we observe this kind of diffusion processes, content diffusion  with respect to the social network at a more mezzal level,  where we look at the influence of being a bridge between communities,  and in this case empirically we see, according to this measure,  which you may interpret in terms of being a weak link or not,  that weak links are maximizing or correspond to a maximum number of transmissions.  So basically being connecting two remote, but not two remote communities is of importance.  This kind of debunking the notion that a weak link may always be favoring diffusion  because after a certain level there is no more influence of that.  Maybe to finish with the two minutes that I have,  going a little bit further on the social semantic influence effects,  there's this study by Shia and colleagues two years ago on Twitter,  where they've been looking at, again, this influence effects in terms of retweeting somebody  or mentioning somebody, or also simply having some authority,  which is measured by the indegree that the Twitter blogger has,  trying to understand how these different dimensions correlate.  Is there a correlation between the indegree that you have, the incoming links,  and the number of times you get retweeted, or the number of times you get mentioned,  which are also two different influence effects.  And so what they found basically is that there is no observable correlation  between a high-degree node and the number of retweets.  So this Venn diagram, for instance, shows that if you take the top 100 influentials,  in terms of retweets, mentions an indegree, there is little overlap between those top influentials.  But essentially, apart from these structural effects on the diffusion,  you also have a semantic effect on the diffusion,  which means that if you look at the Twitter blogger, and if you look at the set of topics that they address,  you see that there is a boost of influence for Twitter users  who are basically narrowing their discourse on a small set of topics, essentially,  except for a few exceptions.  So what this means is that there is essentially an impact of the semantic properties  of the originator on the influence, and not only social network features.  But this is also one way to appraise this kind of social-seminic dynamics  that are not strictly social and not strictly separable, one from the other.  And I won't...  Let's say we'll skip to the end.  That is, that essentially, apart from this overview,  what we are seeing now is that we are going towards an experimental science  of social-seminic dynamics that is possible with the social-technical systems,  and while we have quite some good knowledge on the social network features,  we are really relatively blind, again empirically,  regarding the local cognition processes,  how this information is being transformed from a user to the other,  or even just the ontology of what we are talking about.  What are we talking about?  Are we talking about terms being diffused?  Are we talking about sentences?  Are we talking about notions?  There is a lot of work to do here.  And so I'll be welcoming questions.  Thanks for your attention so far.  Thank you very much.  APPLAUSE  Great talk.  Are there questions?  Okay, then let me ask a question.  So one interesting phenomenon I can see is,  and I think it's an interesting discussion to have  in an agent-based modeling conference, right?  So digital humanities is not as universal as it says it is,  because it's basically 90% linguists.  And I have the same feeling for the semantic web community right now.  It's actually 90% sociologists.  So if we talk about,  and this is actually an interesting mixture, right?  So there's the semantic and linguist people  who know how to datamine semantic words and whatever,  and there is the social guys who datamine people.  And the interesting thing is that there is differences in the perspective,  not only in terms of using real data and whatever,  but so for example, you called power laws stylized facts,  which a physicist would call not a universal law,  but something he would look at as a universal law,  while you were looking at regularities,  like the daily drunk pattern, or the weekly drunk pattern,  and you called that predictive,  which the physicists would deny, right?  Because you would say, okay, patterns you can find everywhere,  datamining is only taking you somewhere.  So one thing I also would like to throw in the audience is,  is basically what are the things to be gained  by actually abolishing the prejudices against each other,  and actually what are the key ingredients.  So in addition to what you have shown, what's all valid,  it's all nice, are we missing something?  Is there beyond cultural anthropology,  beyond data miners and physicists,  what are the fields we actually would need to do this  and take it to the next level?  You mean the social science field, right?  The social semantic kind of field,  where we have linked data of any kind, right?  Which is basically what it is, in a sense.  Yeah, well, what you can basically say is that there is actually no,  for the moment, there is no widespread knowledge of this big data thing,  or even the modelling and evores in the social science communities.  So essentially I would say that you'd need to attract,  or even like to get knowledge of the communities that are relevant here,  you would necessarily have to start a pedagogical effort  towards the few people who might be at the interface,  because there are also many communities  who are not interested into these high level,  or large scale characterizations.  So essentially you have very few communities,  for instance in anthropology,  that would be ready to listen to this kind of discourse.  Except for the specific people who have been working on this naturalization,  of cultural dynamics, or social semantic dynamics,  and who are ready to introduce models into their field.  And they are actually doing that for the moment.  But it would be very difficult to map the communities  that are relevant in the social science fields,  because they are, for the moment, restricted to individuals,  rather than sub-fields, actually, breast-pings.  So, that's a pretty good question.  Your data are related to social networks,  like Twitter or so,  but you mentioned also scientific systems,  and what is missing, I feel, is relation of power.  Do you think that this relation can be built into by connectivity?  What is missing, sorry, I didn't understand.  It's relations of power.  Relations of power.  Well, that's part of the authority proxy, in some way,  that you are looking at.  For the moment it's kind of a superficial outlook,  because it really tries to describe the main determinants of authority,  or influence effects,  that could be at least quantitatively at a statistical level,  described by simple structural features.  The notion of relations of power,  in the case of scientific communities,  that would be really relevant,  because then you could introduce parameters that relate to this.  In the case of online communities, it is also relevant,  but it's often outside of the observational scope,  because that, for instance, the credibility of an individual,  and the outside credibility of an individual,  is difficult to describe,  to deduce from these traces.  So that's also, I think, it touches to what are the questions,  that we can ask, given the data that we have.  And in this case, the power relationship,  I think they are beyond the general research program that is here,  at least for the moment.  Okay, one last question.  By the way, if anybody has the other microphone, please.  Well, I'm Tartan, come from University College London.  I found this talk is very interesting, maybe my,  because this area is relatively new for me.  My question is really, what's real,  you can think of the potential application  of understanding this social semantics.  I give the example you gave about the drunk.  I think it's very kind of, kind of an obvious example,  because drinking behavior, I mean,  we normally say in the wake-end,  people do drink more than normal,  so we can say not one can be useful for prediction.  But if you bring that example to this broad term of social semantics,  so what do you think would be possible, as you said,  in the point here, the first one,  we lowered good knowledge about the morphology power law thing.  But how are we going to move to the next stage?  I'm going to make a useful thought for that.  Yeah, I'll start with, I guess, a very qualitative question,  which is one that is, for instance, let's stay in the web,  and there is a typical political science  and also sociology question now,  which is, are the online interactions creating a vulcanized world  where everybody goes towards content that they are agreeing with,  that they are familiar with, and that with the internet,  we are witnessing the emergence of isolated semantic communities.  So, is it the case that would be one of the first possibilities  of this kind of studies,  how could it be avoided in some way, or weakened,  if this is what we are looking for?  And so that would open to normative models  where we'd like to favor some kinds of behaviors  or connections that would mitigate this kind of effect.  That could be one of the questions here.  You'd have, let's say, a political blogosphere  that is pretty well segmented with people who are getting fed  with content or opinions that they are agreeing with,  and so what is the structure of these communities  and are there some discussion paths that are possible  and what is the implication on the political debate in general,  especially if most of the political debate  or a large part of the political debate moves to the internet.
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So Camille Roth, social semantic frameworks for techno-social systems. Thank you. So what I'd like to do today is to provide you with an overview of where we are standing at in terms of understanding the empirical social semantic dynamics, in the case of social technical systems. So what I'm doing is first presenting the main field of research. I'll try to be presenting the main field of research that can inform us on this type of social semantic dynamics. And then I will be presenting the state of the art of precisely the understanding of some empirical works that have shed light on the social semantic dynamics in the last decade, or let's say a couple of decades. And then I'll be presenting the most recent developments that try to precisely address the social semantic dynamics in a joint way, that is understanding the social and the semantic dynamics altogether. So first of all, I try to provide a definition of what I understand by social technical systems, and it's going to be a little bit narrower than what we've been talking about today, or yesterday as well, because it's going to be especially about social technical systems that are relevant for social semantic dynamics. So essentially that's systems where you have agents who are creating knowledge, processing information, exchanging concepts, connecting concepts in a distributed manner, in a decentralized manner, in such a way that as a whole the system produces a kind of operation of social cognition that is distributed over the whole system. So that concerns communities and systems that have been flourishing in the last few years, including online communities essentially, but also systems which have been historically present and also studied over the past decades, like communities of scientists. With respect to the web bloggers and the Wikic contributors, or people interacting online in online forums, or people dealing with tagging systems, posting stuff on Facebook and social networks, we have a much wider base of observation, and this is also why this field experimentally, empirically, proposes new possibilities. So that's going to be the social technical systems I'll be dealing with, and also a specific understanding of social cognition here, which is a little bit different from what we understand in cognitive psychology usually, in the sense that in psychology social cognition is about understanding how individuals react in social contexts. While here social cognition will rather be a notion of what is being done, what is being achieved by a system of people who are all together in a distributed manner, processing information, and at this level, at the system level, doing a cognition operation. So there are essentially three main streams of research, most relevant here, that could inform us on this, and I'll start with the most theoretical, that is not necessarily directly implementable in empirical terms, which is social and applied epistemology with three main subtopics, and the first one being understanding social factors influencing individual knowledge, like for instance, where does authority come from, how does it come that we are believing this fact and not that fact, and does it depend on the properties of the person who expresses these facts, or is there a social setting that explains this authority phenomenon. That's one stream that is relevant here, another stream which is particularly relevant, which is related to the organization of cognitive labor, like how within a system, within a group, cognitive operations are being distributed across agents, and in this sense, you can also find a term of distributed cognition, that's been also applied to scientific communities, where you see the process of the whole scientific community producing scientific knowledge as a distributed cognition operation, where each individual member is actually working on a specific subtopic and as a whole, without necessarily knowing it, or at least without having a centralized control, does this cognition operation. And there's also the third stream which is pretty much unrelated here, which is the notion of collective knowledge, or at least as the less linked with us, which is how does some piece of knowledge become a collective knowledge, that is how does it come that some proposition P is known by everybody, and can we assert the notion of collective knowledge as equivalent to this kind of formal logical proposition that this is a collective knowledge, if and only if everybody knows. So that's about social and applied epistemology, and that's probably the least connected to empirical things, because in general, this is about case studies that are not necessarily being developed on a wide scale. The second field is cultural anthropology, and this is much more related to the social-seminic dynamics, because cultural anthropologists have been interested in general at the fact that there is some similarity in cultural systems, that there are some subgroups, some aggregates of people who are actually having the same beliefs, that they are sharing the same norms, and the question is how does it come that we have individual interaction processes, and that as a whole, there emerges some similarity. So in this case, they are considering culture as acquired information, and the question is what are the microprocesses that lead to this macro-observation that there is some sort of stability. And maybe I'll describe just two, let's say, iconic theories in that field, one which is probably well known to you, which is mimetics, which basically assumes that there are memes, that there are atomic items of knowledge, that they are being replicated from agent to agent through a Hi-Fi process, it's being just basically copied and pasted from the brain to another brain, and then assuming that there is some fitness and some representations, the most, the fittest representations are basically getting more, better propagated. So this has opened the way to trying to validate empirically this kind of social-seminic dynamics, and has also received a lot of critics from the anthropologists themselves, as they are not entirely convinced that there is such a thing as an atomic item of knowledge, and also that there is not necessarily a process of Hi-Fi replication. So in this case, the second iconic theory, which is perhaps a little bit less known, is one introduced by Dan Sperber in 1996 that was assuming the existence of cultural epidemiology in the sense that cultural items or cultural representations are being propagated through a re-transformation process. What I'm telling you now is being interpreted by each one of you in their own terms and being reformulated at this level. There are some things that are understood, some things that are understood differently, and then this kind of re-transformation process may not be compared with a Hi-Fi replication thing. So there is the notion that there is a cultural, the notion of cultural representation basically is understood as a set of varying sub-representations, and that representation is basically the set of all its variants. The typical example here being the tale of the little red riding hood that has several versions that are all differing a little bit, and essentially the cultural representation little red riding hood is the set of all its variants. And underlying this, there is the assumption that there are attractive cognitive attractors that favor the transformation of some representations into some other representations, and this is what should lead to cultural similarity. The main, let's say the bottom line here with these two theories is that empirically there have been a few realistic attempts to validate them using real data. So at the moment, even if very recently there have been some progress in this field, this cultural anthropological theory have remained relatively unvalidated at the level of precision that they are providing. And so the third stream of research that's relevant here is much more recent, is deemed by some as being social computing. This is basically not necessarily related to social semantic dynamics, but essentially the aim of this research field is to infer information from the dynamics of social systems from large data sets, kind of big data, and of war, that goes from very basic things such as this minimal understanding of social computing as social sensing of the status of a system at a certain time, as the, maybe the participical example here would be Google Flu Trans, where it's possible to predict or to say what the state of the system is before the information gets aggregated at the institutional level. So there are social sensing of the current status of the system. There's also the possibility of trying to predict what the social system will be doing. And essentially again, from large data sets, statistically you have this paper in 2010 that was working on Twitter data that showed that you could predict the revenue of a movie by looking at the discussions regarding the movie the day it was first shown on screen. And so then there is this predictive and devour that also touches to social computing. There's also more about it in terms of understanding the micro level human dynamics that are behind the states of the system, and for instance understanding human mobility at the local level from large data sets again, describing movements of people. You have this study by Gonzalez and colleagues in 2008 that was based on cell phone data showing where people move in general across the city and across the territory in order to reconstruct the low level behavior of individuals. And in a more, maybe cognitively more interesting way, you have also the possibility of describing collective decision processes. For instance, in this study that was based on Wikipedia, the question was to know what influences the voting behavior of agents, depending on the information they have. So the underlying question here was to observe the traces of the decisions of individuals regarding the election of an administrator on the Wikipedia, and to see if there was an open available to all process of decision, and the underlying question about the human processes was about knowing whether people vote according to what they see other people have voted, so understanding hurting behavior, and also to understand whether people vote for people that they think are more experienced than they are. In this case, it's also possible because on Wikipedia, anyone can see the status of the person they are voting for or against, and see whether this person has much more experience on the Wikipedia or other. The relationship with the political science question is quite clear, whether people are voting according to their neighbors, and people are voting according to the experience that they are assigning to the people they are voting for. And the answer on Wikipedia that was one of the achievements you can get with the social computing is that essentially there is no hurting behavior that people are not voting according to what they have seen in terms of previous votes, and also that there is a very strong impact of the perceived experience of people because of the cut of the screen. There are a lot of differences in terms of edits between the person who votes and the person who is being voted for, and essentially you have much more positive votes for people who have two or three orders of magnitude of edits more than you, and you vote less or more negatively for people who have two or three orders of magnitude below you. So essentially that's how far it can get in terms of social computing. You take these big data sets and then you ask questions to the data sets. It's pretty familiar, but that's the sort of field of research that's really relevant here for the social semantic dynamics. So coming back to the main question, what would be essentially needed for an empirical science based on this in vivo data of these social cognition processes? Essentially we can distinguish two main questions. It's the question of knowing the shape of the interactions, that is the substrate onto which this social cognition occurs, and the second question is knowing after that the dynamics of social transmission, information transmission, the dynamics of contents that occurs on this substrate. And also being able, and that's a very important question here, to be able to describe those cultural items, connecting with the cultural anthropology critics that are pointing towards the non-existence of atomic items and so on and so forth. So there are these two questions. So where do we stand at? In terms of social network, I won't spend too much time on that because I guess everybody is familiar with that. So just a very quick historical hindsight here to see that in fact there is a big difference in terms of advancement between the knowledge of the social shape of interactions and the diffusion of content. There has been a first period of development that actually goes back very early in time in the 40s, and let's say up to the 70s it's been essentially populated by social scientists who were doing mathematical sociology. They were working on small case studies, basically from data that they collected in the field. So essentially 20s, 30s, 40 people maximum, with very qualitative empirical data sets describing the relationships within a certain social group. But still most of the definitions that we are using today are based on this effort of formalization in the case of small networks, essentially centrality, local patterns, betweenness, or even tried to close the notion of communities, were also the focus of these communities of scientists. So that was the first step and then the second period that we know much better. Here is what's been called sometimes the new science of networks that has changed a little bit the focus, moving towards large scale data sets, having a complex system standpoint as you all know, and redeveloping in a much more formal and quantitative way these initial insights on power-lows, communities, also introducing morphogenesis models, which were also present in the 40s to the 70s, but introducing stochastic behavior and agent-based models in a pretty new way. There's just an illustration here of the type of indiverse that we see now, which have a little bit shifted from the very beginning of this new science of networks, where initially we've been looking for universal patterns in those networks, and right now I would say the trend is more to look for the individual differences between specific networks and specific contexts. In this case, for instance, in this very example, in this very paper, we've been looking at the cascade dynamics in the blog network, looking at specific patterns that are really defined at the level of a blog network, for people diffusing content, citing somebody who also diffuse this content that provides these kind of small cascading patterns. And then the question is, given these kind of patterns that are really specific, try to find a morphogenesis model that is also very specific. In this case, you have an agent-based model, very recent, that starts with a random work process that makes it possible to generate posts, and then the posts are possibly linking or not linking to other posts, choosing or not choosing a neighbor, a previous neighbor, and then possibly choosing a post of this non-neighbor or neighbor, and then and so on and so forth. And eventually, these kind of very adapted morphogenesis models make it possible to recreate the distribution of patterns that have been seen initially. So just this quick overview to basically say that in terms of social network, we've got a pretty good knowledge of what happens. We've got two points where we have very sophisticated models in very specific cases. It's less clear that we have the same kind of knowledge and the dynamics of content. There's been a recent work on dynamics of conversations, for instance, online conversations, that also were only possibly observable very recently as well, so it's almost atological to say that this effort is pretty recent. So in this paper, there's just a very first attempt, but it's 2004, to define some simple dynamic features of the usage of a certain term. So it's really basic because it's at the level of a term, and it's been so far several years after that, and it's just trying to create a dichotomy between terms that are chattering or terms that are spiking. So really essentially basic, but then it's been extended further by looking at the dynamics of term usage, differentiating between the source types, in this case the question being, are there different usages between the news, the mainstream news and the blogs, and can we find temporal correlations in the usage of some term, or versus the location when we have geographic data, and these dynamics of term usage can be described in terms of specific locations that use specific terms, so eventually you can imagine some evolutionary linguistics that is also geographically grounded. In this case you see the usage of the variant spelling of hello or hellover, hellover something that occurs in different areas in the US. So going further into the differentiation, there's also pretty much in the line of social computing and divorce being some works that try to predict the usage of terms. That is, now that the system is dealing with some issues, can we predict how it will deal with some issues in the future? So in this case what you have is, for instance, the variations in the usage of drunk that is pretty much regular, you notice that there's a first peak on Friday morning and there's a second peak on Saturday morning and then the highest peak is on Sunday morning. What is most interesting here is that it happens every week. It's probably not a very surprising observation, but it shows that even at this very simple level you can already start to get some prediction about what humans will do next week or at which time. So you also have variations and divergences that are also interesting in this case. For instance, if you look at much less precise terms such as sad and busy, you see models that learn from the data to predict the actual usage from a week to another week. So the prediction is actually the dashed line and the actual observation is the solid line. We see in these two graphs that the prediction works pretty well and actually works every week as well, except for this case of July 7, 2007, that corresponds to English-speaking blogs in the UK on the day of the terrorist attacks. So essentially, in this case, divergence corresponds precisely to a change in the underlying state of the system. So that's also indicative of this kind of variations. So that's the dynamics of terms, but there have been also works that have been trying to extend a little bit this viewpoint because the term is a very simplistic thing and of course, in the cultural anthropologists would not even look at it. So they've been trying to see the dynamics of sentences, finding, for instance, quotations in blog networks and precisely describing how the quotations may vary. The intuition is that the quotation, the quotation of a political figure, for instance, should be the same from a blog to another blog or from a source to another source. But it is the case that even at this level of something that should undergo high-five replication, there is already a lot of variations. So essentially, they gathered all quotations that were similar in terms of edit distance and some of the features in order to group these sentences in clusters that could be pretty much corresponding to this general idea of sparbar, of a cultural representation being the set of all its variants. And so essentially, they've been at this level looking at the dynamics of these concepts at this semantic mezzol level. You also have works on the dynamics of disciplines. Maybe I skip that because it's not necessarily the most important thing at this time. What I want to move to now is to see how this is possible, how could we actually connect the structure and the content. This is the most recent endeavor, trying to empirically describe how opinions or expressions are being present differently in terms of the structure and how they are being diffused differently as well. So in this case, you have a very binary thing, which is the blue are the Democrats and the red are the Republicans. And in this case, this is the US blogosphere in the 2004 that describes the links between the two, between blogs actually in this blogosphere and showing that essentially there are two structurally disconnected communities. That's, in this case, in terms of content, extremely simplistic because we have only a binary indicator, which is Democrat or Republican, but it makes it possible to already combine the social network view with the content that is being discussed on it. Coming back to this discussion, diffusion cascades with these patterns, it is also possible when you look at, again, a blog network to classify a given blog or a given subset of blogs according to the distribution of cascades that you observe in these blogs. The observation in that paper was that when you do an unsupervised categorization of the distribution of patterns in this specific network, you see differences between typical communities in the sense that you can discriminate pretty well. For instance, blogs that are discussing conservative content and blogs that are discussing humoristic content. So in this case, that is quite far from universal observations on the systems because at this level, we even see some dimensions of differentiation between blogs discussing a specific topic and blogs discussing another topic. So that is a conclusion on the social semantic morphology in this case, that there is no universal social morphology in the blog networks, but that it is differentiated semantically as well. To come to a study that we did recently, 2009-2010, where we've been observing the US blogosphere during the presidential primaries, where we've been basically looking at the co-evolution between the social network and the semantic content, the question was to understand whether there was a specific distribution of content that depended on the social network empirically and in the reverse direction whether there was dynamics of the social network that depended on the distribution of content. So the two dynamics jointly co-evolving together. Is it possible to appraise these kind of features? So one of the first observations is that basically, statically, you have a very strong homophily. If you look at blogs in terms of the terms that they are using and in terms of the n-grams that they are using, let's say climate change, taxation reduction, these kind of terms, you can describe blogs as sets of topics that they are addressing. And so you can also describe the semantic dissimilarity that you have between some blogs and some other. And in this case, you see that if you look at the blue curve in terms of the semantic dissimilarity, which goes from zero for blogs addressing exactly the same topics to one for blogs addressing strictly different topics, you have essentially between the connected blogs a much closer semantic similarity. So this is homophily-aposteriority. You observe some sort of homophily-aposteriority once the links have been there. And so the question can be connected to this general cultural anthropologic question, which is where does dissimilarity come from? Or do we have, again, empirical means to observe this in vivo and to try to understand what are the general dynamics of this system? So you can look at the social network dynamics in terms of the social network itself, for instance, looking at the prevention for interaction to occur depending on the distance within the networks, observing that, for instance, you have essentially a very high prevention for repeated interaction at distance one between connected blogs, and then it exponentially decreases up to a certain point where the preference of interaction, the preference, basically, of the social network formation with respect to the social network is not influenced anymore by the distance. In other words, beyond a certain horizon, there is no more influence of the distance, and so the fact that you are at distance five or six of any possible other blogs doesn't change anything. We might speak of a narrow world where all interactions are happening in a narrow circle rather than a small world where all interactions are theoretically possible with any blog that is generally not farther than distance six. So what is more interesting is to combine this social network dynamics with respect to the content and to see, so this is, again, a prevention of interaction that describes how likely you are to interact with some nodes that have some properties or some couple of properties. And when you look at the differentiation between the social distance and the semantic distance, what you see in these 3D graphs is that basically, if you look at these blogs that are far apart, it's like distance three and more, you have a strict monotonic decrease of the prevention of interaction. It is in the farther world, your prevention of interaction essentially depends on the semantic distance. So there is an a priori homophily that you will tend to interact with blogs that are similar, but when you go closer to the closed network that is a distance one, what you see is that essentially this effect doesn't hold anymore. The distance one within the repeated interaction, you can have the same prevention to interact with individual that are also dissimilar. And essentially what is not shown here is that you have also preference for higher degree nodes that is also depending on the semantic distance as well. In the sense that you tend to interact with higher degree nodes when they are essentially dissimilar, but when they are similar, the degree does not influence the interaction. And what is also present here is that there is a contraction of the network after interaction that describes the dynamics of the social network once people have interacted that shows that there is a contraction in terms of the semantic dissimilarity. Once you interact with another blog, you tend to be more similar, but what is interesting is that this phenomenon occurs before interaction as well. Zero is the time where interaction occurs and this is the semantic similarity that shows that about three weeks before the given blogs are going to interact or are going to side each other, they are already starting to get closer in terms of content. So the same happens in the social network in the sense that when you interact, there is a local contraction of the network that also could be observed before the interaction. So that describes just in a nutshell and just to be quick on this, that describes how the social network is evolving depending on these two dimensions, the social network itself and the semantic content. And now we can look at the effect of the social network and diffusion, again with in vivo data. So looking at cascades that are, as I said, a blog saying something and then being cited by other blogs that say the same thing later in the past, where we say that if this kind of pattern occurs, it is likely to be an explicit proof evidence of explicit diffusion. So what we see is that it depends, let's not spend too much time on describing the variables, but basically you have the total attention, which is the amount of attention the blog gets in terms of the share they relatively get from the other bloggers. So it's not the degree, it's really the share of the links that come to you with respect to the number of links that the person that points to you is also distributing to the rest of the system. So for instance, if you have two links and you have one link to one person, you give one half of attention to this person. So you can have aggregated this, what it shows is that at this very local level, the diffusion is influenced by this intention process in a nonlinear way, in the sense that it plateaus, and also it is not growing as fast at the very beginning. So basically for blogs that have little to very little attention, there is the same type of diffusion impact and the same for blogs that have strong to very strong attention, but then there's an intermediate zone where this impact is maximum. More interestingly, we observe this kind of diffusion processes, content diffusion with respect to the social network at a more mezzal level, where we look at the influence of being a bridge between communities, and in this case empirically we see, according to this measure, which you may interpret in terms of being a weak link or not, that weak links are maximizing or correspond to a maximum number of transmissions. So basically being connecting two remote, but not two remote communities is of importance. This kind of debunking the notion that a weak link may always be favoring diffusion because after a certain level there is no more influence of that. Maybe to finish with the two minutes that I have, going a little bit further on the social semantic influence effects, there's this study by Shia and colleagues two years ago on Twitter, where they've been looking at, again, this influence effects in terms of retweeting somebody or mentioning somebody, or also simply having some authority, which is measured by the indegree that the Twitter blogger has, trying to understand how these different dimensions correlate. Is there a correlation between the indegree that you have, the incoming links, and the number of times you get retweeted, or the number of times you get mentioned, which are also two different influence effects. And so what they found basically is that there is no observable correlation between a high-degree node and the number of retweets. So this Venn diagram, for instance, shows that if you take the top 100 influentials, in terms of retweets, mentions an indegree, there is little overlap between those top influentials. But essentially, apart from these structural effects on the diffusion, you also have a semantic effect on the diffusion, which means that if you look at the Twitter blogger, and if you look at the set of topics that they address, you see that there is a boost of influence for Twitter users who are basically narrowing their discourse on a small set of topics, essentially, except for a few exceptions. So what this means is that there is essentially an impact of the semantic properties of the originator on the influence, and not only social network features. But this is also one way to appraise this kind of social-seminic dynamics that are not strictly social and not strictly separable, one from the other. And I won't... Let's say we'll skip to the end. That is, that essentially, apart from this overview, what we are seeing now is that we are going towards an experimental science of social-seminic dynamics that is possible with the social-technical systems, and while we have quite some good knowledge on the social network features, we are really relatively blind, again empirically, regarding the local cognition processes, how this information is being transformed from a user to the other, or even just the ontology of what we are talking about. What are we talking about? Are we talking about terms being diffused? Are we talking about sentences? Are we talking about notions? There is a lot of work to do here. And so I'll be welcoming questions. Thanks for your attention so far. Thank you very much. APPLAUSE Great talk. Are there questions? Okay, then let me ask a question. So one interesting phenomenon I can see is, and I think it's an interesting discussion to have in an agent-based modeling conference, right? So digital humanities is not as universal as it says it is, because it's basically 90% linguists. And I have the same feeling for the semantic web community right now. It's actually 90% sociologists. So if we talk about, and this is actually an interesting mixture, right? So there's the semantic and linguist people who know how to datamine semantic words and whatever, and there is the social guys who datamine people. And the interesting thing is that there is differences in the perspective, not only in terms of using real data and whatever, but so for example, you called power laws stylized facts, which a physicist would call not a universal law, but something he would look at as a universal law, while you were looking at regularities, like the daily drunk pattern, or the weekly drunk pattern, and you called that predictive, which the physicists would deny, right? Because you would say, okay, patterns you can find everywhere, datamining is only taking you somewhere. So one thing I also would like to throw in the audience is, is basically what are the things to be gained by actually abolishing the prejudices against each other, and actually what are the key ingredients. So in addition to what you have shown, what's all valid, it's all nice, are we missing something? Is there beyond cultural anthropology, beyond data miners and physicists, what are the fields we actually would need to do this and take it to the next level? You mean the social science field, right? The social semantic kind of field, where we have linked data of any kind, right? Which is basically what it is, in a sense. Yeah, well, what you can basically say is that there is actually no, for the moment, there is no widespread knowledge of this big data thing, or even the modelling and evores in the social science communities. So essentially I would say that you'd need to attract, or even like to get knowledge of the communities that are relevant here, you would necessarily have to start a pedagogical effort towards the few people who might be at the interface, because there are also many communities who are not interested into these high level, or large scale characterizations. So essentially you have very few communities, for instance in anthropology, that would be ready to listen to this kind of discourse. Except for the specific people who have been working on this naturalization, of cultural dynamics, or social semantic dynamics, and who are ready to introduce models into their field. And they are actually doing that for the moment. But it would be very difficult to map the communities that are relevant in the social science fields, because they are, for the moment, restricted to individuals, rather than sub-fields, actually, breast-pings. So, that's a pretty good question. Your data are related to social networks, like Twitter or so, but you mentioned also scientific systems, and what is missing, I feel, is relation of power. Do you think that this relation can be built into by connectivity? What is missing, sorry, I didn't understand. It's relations of power. Relations of power. Well, that's part of the authority proxy, in some way, that you are looking at. For the moment it's kind of a superficial outlook, because it really tries to describe the main determinants of authority, or influence effects, that could be at least quantitatively at a statistical level, described by simple structural features. The notion of relations of power, in the case of scientific communities, that would be really relevant, because then you could introduce parameters that relate to this. In the case of online communities, it is also relevant, but it's often outside of the observational scope, because that, for instance, the credibility of an individual, and the outside credibility of an individual, is difficult to describe, to deduce from these traces. So that's also, I think, it touches to what are the questions, that we can ask, given the data that we have. And in this case, the power relationship, I think they are beyond the general research program that is here, at least for the moment. Okay, one last question. By the way, if anybody has the other microphone, please. Well, I'm Tartan, come from University College London. I found this talk is very interesting, maybe my, because this area is relatively new for me. My question is really, what's real, you can think of the potential application of understanding this social semantics. I give the example you gave about the drunk. I think it's very kind of, kind of an obvious example, because drinking behavior, I mean, we normally say in the wake-end, people do drink more than normal, so we can say not one can be useful for prediction. But if you bring that example to this broad term of social semantics, so what do you think would be possible, as you said, in the point here, the first one, we lowered good knowledge about the morphology power law thing. But how are we going to move to the next stage? I'm going to make a useful thought for that. Yeah, I'll start with, I guess, a very qualitative question, which is one that is, for instance, let's stay in the web, and there is a typical political science and also sociology question now, which is, are the online interactions creating a vulcanized world where everybody goes towards content that they are agreeing with, that they are familiar with, and that with the internet, we are witnessing the emergence of isolated semantic communities. So, is it the case that would be one of the first possibilities of this kind of studies, how could it be avoided in some way, or weakened, if this is what we are looking for? And so that would open to normative models where we'd like to favor some kinds of behaviors or connections that would mitigate this kind of effect. That could be one of the questions here. You'd have, let's say, a political blogosphere that is pretty well segmented with people who are getting fed with content or opinions that they are agreeing with, and so what is the structure of these communities and are there some discussion paths that are possible and what is the implication on the political debate in general, especially if most of the political debate or a large part of the political debate moves to the internet.
where you have agents who are creating knowledge, together in a distributed manner, processing information, and at this level, at the system predict what the social system will be doing. And Essentially we can distinguish two main dynamics of social transmission, information communities of scientists. So that was the first differences between specific networks and types, in this case the question being, are there observations on the systems because at this social network and the semantic content, the of content. So the two dynamics jointly that are not strictly social and not strictly discussion to have in an agent-based modeling many communities who are not interested into