Do Social Media Polarize?
Müller, Phillip
One of the most heavily discussed questions in the context of belief polarization in recent times, is the contribution of social media technologies. The infamous “filter bubble” hypothesis suggests that that social media algorithms are, by design, driving ideological as well as affective polarization in users. Even though empirical evidence on this mechanism is mixed, at best, this assumption has become a fixture of any debate on polarization. Drawing from a secondary analyses of large-scale panel data and a systematic literature review of empirical research on social media’s polarization effects (both work in progress), I will argue that the question whether social media contribute to polarization has to be answered much more nuancedly. Findings of the two studies indicate that we are far from having consistent evidence on polarizing effects of algorithmic content selection. Rather, social media related polarization effects seem to result from users’ own content selection choices and their active participation in social media communications (i.e., posting, sharing, liking, etc.).
 Maybe I should first introduce myself. I'm a scholar of media and communication studies at the University of Mannheim. So my main focus is on political journalism and how it shapes attitudes and behaviors. I did a lot of research on political populism in communication, but this is not what I'm going to talk about today. But rather I will be focusing on a new line of thinking that keeps me busy in recent months or years, which is the social media polarized. And my focus since I am a communication researcher is mainly on the filter bubble hypothesis, which is very prominent in our field. So I think this is not exactly what Stephen talked about. This is where the complementarity is. Sorry about that. That should have been animated differently. So I don't have to... Yeah? Did they just miss the last name? No, I don't have to. I don't have to type a gun. I don't think I have to talk a lot about this anymore because we already heard this in the keynote talk this morning. There is an empirical observation that especially effective polarization, as you said, and already showed us with some data, is increasing, not in all countries, mainly in the US. Maybe that is a little bit of a limitation also to my research because my empirical research is based on German data. And we can, of course, and this is maybe a point of discussion that I will come back to in the end, question whether that is actually a most important aspect for German political communication, whether there are polarization effects or not. But we can leave this open for the moment. Now, of course, the question is, and that is also the intention of the track that this talk has been placed by the organizers, what are the roots of this increase of polarization? And there are a couple of, I would say, lines of discussion in the literature. And I'm sticking pretty close to the literature from the field of political psychology, I would say, which is very dominant also in my discipline when it comes to arguing about polarization. And there I see three lines of reasoning, social identity-based explanations, ideology-based explanations, and more or less information exposure or information behavior-based explanations. And that is, of course, where the whole filter bubble argument is situated. So the idea is that social media might be driving polarization, and that is, of course, also an idea which is discussed a lot in public discourse on polarization. So we see a lot of newspaper coverage on the question of how platforms might have contributed to the development of polarization in our societies, and also a lot of non-academic societal discourse in institutions in the political sphere on the road of platforms here. And I think one very famous argument, and I'm very happy that this was not central to Stephen's talk, is the filter bubble hypothesis developed by Eli Perizer, a journalism scholar from New York. And he says that algorithmic content selection is based on users' political preferences, and because of that, the algorithm will show users what they are already interested in. And that means that partisanship consistent information environments might develop. And the argument is this does not stay without any effects. And the effect that is assumed is that partisan ties will increase further, and effective polarization will be promoted by algorithmically shaped content selection. Now the first problem is, the first empirical problem is that, of course, it's very difficult to study these kinds of questions. I think Stephen made that clear already. But there are some studies which have been trying to figure out whether filter bubbles actually occur. And I mean, they are quite heterogeneous in terms of their methodological approach. So it's really difficult to draw general conclusions from these studies. But I think as far as the evidence that I know goes, there is no really convincing evidence that algorithmic selection really creates those filter bubbles. So that people are really more and more confronted with only like-minded views if they are using algorithmic content selection systems. But I admit that this is a very open question. We are in an urgent need of more research about that. And now what I did, myself, together with a PhD student, Katarina Ludwig, is first a systematic lit review. And I think there might, there will probably be a lot of overlap, at least in the bodies of studies that we looked at. I mean, our question was a bit different than yours. We did not only focus on causal effects, but we said we focused on all studies that include social media use as an input variable, and then either effective or ideological polarization as it's usually operationalized in the political psychology literature as the dependent variable. And yeah, but we also looked at studies using cross-sectional data. So it's a bit of a different approach. And as I understood at your lit review, is also about other dependent variables. So it's a bit different in terms of its approach. And I think most findings are somewhat similar. This is also in preparation. The manuscript is not finished yet, so we are not able to submit it at this moment, but we are working on it. Now what are our first insights? First, my impression, at least, is if you use a broad understanding of polarization and look for empirical studies, then you find that the research landscape is to some degree conceptually blurry because studies, maybe that's a bit harsh, tend to an idiosyncratic understanding of polarization, but there are many different ways of conceptually understanding polarization and also empirically measuring polarization out there. So concise conclusions are, in my impression, difficult to draw. The body of literature that we ended up with were 88 empirical studies. We also had some hundreds in the first round of sampling, but we narrowed that down to 88 empirical studies that have been published and only 31 studies we found did investigate polarization effects of social media at the user level. Even though I admit we have to be careful about the term effects because it's not only causal evidence in the narrow sense that Steven's literature review deals with. Now we find that the most frequent ways of operationalizing social media use in that context is that either studies use survey measures of the amount of social media use, which is a very coarse measurement, I think, because it does not really tell us what people are confronted with when turning to social media and also does not tell us anything about what people do with social media. Then we also find a lot of experiments which actually look at the effects of pro-encounter attitudinal information exposure. And social media, in that respect, is only like framework. So they use stimuli, oftentimes also non-interactive stimuli, that have a Facebook logo at the top, but the same experiment could have been conducted without any social media framework around it. So we can question whether these effects that are observed in such studies are actually social media effects or not simply information exposure effects. And we found, because this was our specific focus, and we looked for that, only five studies investigating effects or relationships of using recommender systems with polarization. So this is really an area that is very central to the public debate, but is hardly being studied empirically, probably because it's so difficult to study it. Now which patterns of results did we find? First of all, my or our impression was that there is a heterogeneous landscape of evidence on all questions. So there are studies finding polarization effects. There are also studies finding depolarizing effects. And also null findings studies, or at least null findings on specific hypotheses within those studies. So it's not that you could really say there is one central line of findings that is really clear. Also our impression was, or our finding was that the occurrence of any social media effects is often moderated by the strength of partisan ties and also other political attitude or political attitudinal variables. So that means that not all users, and that is actually to psychologists of course pretty self-evident, not all users are affected by social media in the same way. Also we found that there is a lack of research from multi-party systems, which is our justification of course then to look at the German case empirically, but at the same time that could turn into a boomerang if we admit that there's simply more polarization going on in two-party systems like the US. So yeah, it's a bit ambiguous to stress this point I guess. Particularly with regards to effective polarizations, there is more research on ideological polarization from multi-party systems. Our most stable finding was that social media users are more ideologically polarized and that is something that has been shown for different party systems, but this is in most studies not a cause of finding but a correlation of finding. So it's unclear where the causality is. In terms of effective polarization, our impression was that it's not unequivocally, there is not one clear direction of effects in the studies in terms of polarization, but there are studies that establish a causal relationship between social media use and effective polarizations and also studies that fail to do so. So we do not see clear evidence here. Also no clear evidence for recommender system effects. So the filter bubble hypothesis is far from being supported at the moment, but at the same time I have to say there is simply not enough substantial empirical evidence to draw a conclusion whether that is actually an effect of lack of evidence or a substantial effect. Now for the whole filter bubble debate I see two lines of or two major tasks that we have to engage in from that point. The first one is analyze algorithm and other social media effects on polarization competitively. So my notion always is that the algorithm is not all that social media is about, but there is a lot of user behavior going on on social media. Users are tailoring their information environments by themselves and not only the algorithm does that, so there are other things going on in social media use that are not really connected to the algorithm and we have to look at them competitively. And the other thing is that I think we need to analytically isolate algorithm effects that is really not look at social media use in general as a predictor of polarization but really try to figure out what is the algorithm doing to users and explore maybe also different ways of how an algorithm could work and select content for users in more depth. Now I would like to, and this has a little bit of a workshop character because these are two studies that have not been published yet just as the lit review. I would like to present you some preliminary results of studies that I did going in those two directions. The first one was on the competitive assessment of social media effects of different patterns of users behavior and for that, oh no, first what have been my conceptual ideas about that. So that is still very coarse but I think it's more fine-grained than what much research offers us for ideas on how social media might affect polarization. The first thing is the infamous filter bubble hypothesis, so effects of algorithmic content selection that lead to content environments that are really in line with people's political attitudes. Then there is a second effect and that context of passive content exposure via social media that could occur and that is selective exposure. So users are choosing, are actively selecting which accounts to follow, which friends they have and that might also contribute to the like-minded information environment. In some parts of the literature this is referred to as the echo chamber hypothesis. But then there is also active social media use and I think that might be very important for psychologists particularly because also active user behavior on social media might contribute to the shaping of polarization and two ideas I've got there, maybe there are more than that, is social media self-effects. So the argument that if you're posting about something, that you're giving some political content alike, that because of consistency of our strife for internal consistency might increase us or might strengthen us in our own beliefs and that is a hypothesis of self-radicalization through behavior and maybe reflection of that behavior. And then there's also feedback effects that might occur, so that is the social interaction within social media platforms that you see. If you're posting radical content you get more likes and that might increase your own political convictions in terms of polarization. Now the first empirical study that I did try to more or less disentangle these different mechanisms even though I have to admit that that was not possible perfectly with the data at hand. What I did was a secondary analysis of the German longitudinal election studies from the last Bundestagswahl, so we just had an election in Germany a couple of weeks, no, last week. Last week this one was the data from the 2017 election and I looked at effective polarization only here. So how did evaluation of the own party camp develop over the course of the campaign? There were I think seven panel waves that, no, six panel waves, waves two to seven that went into this analysis and it was possible to reconstruct how partisans, so people who liked a different or one specific party a lot, at the beginning of the campaign, how their polarization developed over time, over the course of the campaign. And there were different social media variables included in this data set as well that enabled me to disentangle effects of passive exposure to political posts, of following party-owned accounts, of the frequency of sharing political posts, so active user behavior and of commenting, on the development of effective polarization over time. And what I found was maybe in with regards to time, I would skip the regression results here and come to my summary in words. What I found was that first party evaluations were mostly stable over the course of the campaign, so this again tells us something about effective polarization in Germany or maybe in multiparty systems more generally, that this might not even be the most pressing problem in this kind of system. And I think the current election results might argue in that direction as well. That passive exposure to political posts tended to stabilize party evaluation, particularly so among partisans of right-wing populist AFD and neoliberal FTP, so there were the strongest social media effects. And this, in my reasoning, suggests that there were no algorithm-cost polarization effects at work because we were able to look at selective exposure and active social media use variables distinctively, so I think the variance that remains to be explained by the general amount of exposure to political content via social media might be the ones or might be closer to the actual variance that is being caused by the algorithm. So this could be interpreted in this direction even though it's still only an approximation to algorithm effects. For the following of party-owned accounts, which is selective exposure, and also for active commenting, we saw an increase of effective polarization, again, only among AFD and FTP partisans. For the other parties, partisans, there were literally no effects at all. So this suggests selective exposure and social media self-effects or social impact effects being at work here. Now, for the other direction that I argued we should look into, this is even more at the initial stage. This is an experiment that has just been in the field where we looked at the way how different types of news recommendation systems, which are actually running, influence polarization at the user level. So this is data that I just analyzed this week. This is really preliminary. It might be that the results look different after maybe including additional covariates or so. So what we do here is, this is a large-scale project with colleagues from KIT Karlsruhe. What we do here is that we use different types of news recommendation systems, content-based recommendation systems. So in the first place, recommenders that only look at which texts are similar to each other, and we want to include user variables in the next steps. So there will be additional experiments where we also look at demographic recommendation as the data science colleagues call it, and see how these might affect polarization in comparison to random recommendations. So we have a body of articles, about almost 4,000 articles on migration, and we let the users in four iterations choose from a selection of articles, one article that they are supposed to read, and this is being repeated four times. And either these articles, and this is how the surface of the front end looks like for choosing these articles, and these articles are either randomly picked or they are recommended by a recommender system, and that is our approximation to the algorithm. This does not look like Facebook, I know. But at least content-based recommendation is at work here, and there are not really many studies that actually do that. Now very quickly to our results, because I'm running out of time. In terms of effective polarization, we find that using any kind of recommender system led to less effective polarization as compared to reading randomly selected articles. And that suggests at least for content customization, there might even be depolarization effects. Because the algorithm selects articles for me that are consistent with my convictions, I might be less polarized as compared to the case where I'm confronted with articles that are simply randomly selected and therefore might contradict my own opinion stronger. So there might even be positive news here. But we also see that recommender systems that have a negative sentiment bias. So that is of course closer to what Facebook and Twitter do because they consider the emotionality of posts because these posts receive more interactions, the ones that are more emotionally charged. So systems with negative sentiment bias increase effective polarization. But there is an interaction effect at work here. This only occurs if people spend more time on the content that they are confronted with, not if they have been simply clicking through the articles that we confronted them with. In terms of ideological polarization, we hardly find any effects in this particular study. The only assumption was that a recommender system with a balanced sentiment tended to increase ideological polarization. That might be similar case as above if I'm confronted with content that is not completely in line with my convictions, then this might lead to polarization. Now some concluding remarks my last slide. My impression is, and this is really subjective and personal, that the field suffers from conceptual heterogeneity and empirical inclusiveness or large empirical gaps as you want to put it. I believe there are important gaps if I think of the filter bubble question and whether it has any effects. In particular with regards to the isolation of algorithm cost polarization effects because that argument is really strongly emphasized in public debate. But I think we are still very much picking in the dark here. And also gaps concerning the effects of selective exposure in active social media use, which is a bit in the background of the current debate, I think. My impression is that in the bigger picture algorithmic content selection rather seems to stabilize or depolarize partisans if you isolate only the effect of algorithmic content selection and that observable social media effects might, on polarization, might not mainly be caused by the algorithms and the platform architectures, but rather by user's behavior on the platforms and with the platforms and that, of course, also includes the leaders, as you called them, Steven. So the users that have a lot of followers and that are large-scale content producers, of course. But I admit, as I said a couple of times already, there is not enough empirical research to really be sure about those conclusions. So this is only my first impression. Thanks a lot for listening. I hope that I took a bit too much time.
 Maybe I should first introduce myself.  I'm a scholar of media and communication studies at the University of Mannheim.  So my main focus is on political journalism and how it shapes attitudes and behaviors.  I did a lot of research on political populism in communication, but this is not what I'm  going to talk about today.  But rather I will be focusing on a new line of thinking that keeps me busy in recent months  or years, which is the social media polarized.  And my focus since I am a communication researcher is mainly on the filter bubble hypothesis,  which is very prominent in our field.  So I think this is not exactly what Stephen talked about.  This is where the complementarity is.  Sorry about that.  That should have been animated differently.  So I don't have to...  Yeah?  Did they just miss the last name?  No, I don't have to.  I don't have to type a gun.  I don't think I have to talk a lot about this anymore because we already heard this in the  keynote talk this morning.  There is an empirical observation that especially effective polarization, as you said, and already  showed us with some data, is increasing, not in all countries, mainly in the US.  Maybe that is a little bit of a limitation also to my research because my empirical research  is based on German data.  And we can, of course, and this is maybe a point of discussion that I will come back to  in the end, question whether that is actually a most important aspect for German political  communication, whether there are polarization effects or not.  But we can leave this open for the moment.  Now, of course, the question is, and that is also the intention of the track that this  talk has been placed by the organizers, what are the roots of this increase of polarization?  And there are a couple of, I would say, lines of discussion in the literature.  And I'm sticking pretty close to the literature from the field of political psychology, I  would say, which is very dominant also in my discipline when it comes to arguing about  polarization.  And there I see three lines of reasoning, social identity-based explanations, ideology-based  explanations, and more or less information exposure or information behavior-based explanations.  And that is, of course, where the whole filter bubble argument is situated.  So the idea is that social media might be driving polarization, and that is, of course,  also an idea which is discussed a lot in public discourse on polarization.  So we see a lot of newspaper coverage on the question of how platforms might have contributed  to the development of polarization in our societies, and also a lot of non-academic  societal discourse in institutions in the political sphere on the road of platforms  here.  And I think one very famous argument, and I'm very happy that this was not central to  Stephen's talk, is the filter bubble hypothesis developed by Eli Perizer, a journalism scholar  from New York.  And he says that algorithmic content selection is based on users' political preferences,  and because of that, the algorithm will show users what they are already interested in.  And that means that partisanship consistent information environments might develop.  And the argument is this does not stay without any effects.  And the effect that is assumed is that partisan ties will increase further, and effective  polarization will be promoted by algorithmically shaped content selection.  Now the first problem is, the first empirical problem is that, of course, it's very difficult  to study these kinds of questions.  I think Stephen made that clear already.  But there are some studies which have been trying to figure out whether filter bubbles  actually occur.  And I mean, they are quite heterogeneous in terms of their methodological approach.  So it's really difficult to draw general conclusions from these studies.  But I think as far as the evidence that I know goes, there is no really convincing evidence  that algorithmic selection really creates those filter bubbles.  So that people are really more and more confronted with only like-minded views if they are using  algorithmic content selection systems.  But I admit that this is a very open question.  We are in an urgent need of more research about that.  And now what I did, myself, together with a PhD student, Katarina Ludwig, is first a  systematic lit review.  And I think there might, there will probably be a lot of overlap, at least in the bodies  of studies that we looked at.  I mean, our question was a bit different than yours.  We did not only focus on causal effects, but we said we focused on all studies that include  social media use as an input variable, and then either effective or ideological polarization  as it's usually operationalized in the political psychology literature as the dependent variable.  And yeah, but we also looked at studies using cross-sectional data.  So it's a bit of a different approach.  And as I understood at your lit review, is also about other dependent variables.  So it's a bit different in terms of its approach.  And I think most findings are somewhat similar.  This is also in preparation.  The manuscript is not finished yet, so we are not able to submit it at this moment, but  we are working on it.  Now what are our first insights?  First, my impression, at least, is if you use a broad understanding of polarization and  look for empirical studies, then you find that the research landscape is to some degree  conceptually blurry because studies, maybe that's a bit harsh, tend to an idiosyncratic  understanding of polarization, but there are many different ways of conceptually understanding  polarization and also empirically measuring polarization out there.  So concise conclusions are, in my impression, difficult to draw.  The body of literature that we ended up with were 88 empirical studies.  We also had some hundreds in the first round of sampling, but we narrowed that down to  88 empirical studies that have been published and only 31 studies we found did investigate  polarization effects of social media at the user level.  Even though I admit we have to be careful about the term effects because it's not only  causal evidence in the narrow sense that Steven's literature review deals with.  Now we find that the most frequent ways of operationalizing social media use in that  context is that either studies use survey measures of the amount of social media use,  which is a very coarse measurement, I think, because it does not really tell us what people  are confronted with when turning to social media and also does not tell us anything  about what people do with social media.  Then we also find a lot of experiments which actually look at the effects of pro-encounter  attitudinal information exposure.  And social media, in that respect, is only like framework.  So they use stimuli, oftentimes also non-interactive stimuli, that have a Facebook logo at the  top, but the same experiment could have been conducted without any social media framework  around it.  So we can question whether these effects that are observed in such studies are actually  social media effects or not simply information exposure effects.  And we found, because this was our specific focus, and we looked for that, only five studies  investigating effects or relationships of using recommender systems with polarization.  So this is really an area that is very central to the public debate, but is hardly being  studied empirically, probably because it's so difficult to study it.  Now which patterns of results did we find?  First of all, my or our impression was that there is a heterogeneous landscape of evidence  on all questions.  So there are studies finding polarization effects.  There are also studies finding depolarizing effects.  And also null findings studies, or at least null findings on specific hypotheses within  those studies.  So it's not that you could really say there is one central line of findings that is really  clear.  Also our impression was, or our finding was that the occurrence of any social media effects  is often moderated by the strength of partisan ties and also other political attitude or  political attitudinal variables.  So that means that not all users, and that is actually to psychologists of course pretty  self-evident, not all users are affected by social media in the same way.  Also we found that there is a lack of research from multi-party systems, which is our justification  of course then to look at the German case empirically, but at the same time that could  turn into a boomerang if we admit that there's simply more polarization going on in two-party  systems like the US.  So yeah, it's a bit ambiguous to stress this point I guess.  Particularly with regards to effective polarizations, there is more research on ideological polarization  from multi-party systems.  Our most stable finding was that social media users are more ideologically polarized and  that is something that has been shown for different party systems, but this is in most  studies not a cause of finding but a correlation of finding.  So it's unclear where the causality is.  In terms of effective polarization, our impression was that it's not unequivocally, there is  not one clear direction of effects in the studies in terms of polarization, but there  are studies that establish a causal relationship between social media use and effective polarizations  and also studies that fail to do so.  So we do not see clear evidence here.  Also no clear evidence for recommender system effects.  So the filter bubble hypothesis is far from being supported at the moment, but at the  same time I have to say there is simply not enough substantial empirical evidence to draw  a conclusion whether that is actually an effect of lack of evidence or a substantial effect.  Now for the whole filter bubble debate I see two lines of or two major tasks that we have  to engage in from that point.  The first one is analyze algorithm and other social media effects on polarization competitively.  So my notion always is that the algorithm is not all that social media is about, but  there is a lot of user behavior going on on social media.  Users are tailoring their information environments by themselves and not only the algorithm does  that, so there are other things going on in social media use that are not really connected  to the algorithm and we have to look at them competitively.  And the other thing is that I think we need to analytically isolate algorithm effects  that is really not look at social media use in general as a predictor of polarization  but really try to figure out what is the algorithm doing to users and explore maybe also different  ways of how an algorithm could work and select content for users in more depth.  Now I would like to, and this has a little bit of a workshop character because these  are two studies that have not been published yet just as the lit review.  I would like to present you some preliminary results of studies that I did going in those  two directions.  The first one was on the competitive assessment of social media effects of different patterns  of users behavior and for that, oh no, first what have been my conceptual ideas about that.  So that is still very coarse but I think it's more fine-grained than what much research  offers us for ideas on how social media might affect polarization.  The first thing is the infamous filter bubble hypothesis, so effects of algorithmic content  selection that lead to content environments that are really in line with people's political  attitudes.  Then there is a second effect and that context of passive content exposure via social media  that could occur and that is selective exposure.  So users are choosing, are actively selecting which accounts to follow, which friends they  have and that might also contribute to the like-minded information environment.  In some parts of the literature this is referred to as the echo chamber hypothesis.  But then there is also active social media use and I think that might be very important  for psychologists particularly because also active user behavior on social media might  contribute to the shaping of polarization and two ideas I've got there, maybe there  are more than that, is social media self-effects.  So the argument that if you're posting about something, that you're giving some political  content alike, that because of consistency of our strife for internal consistency might  increase us or might strengthen us in our own beliefs and that is a hypothesis of self-radicalization  through behavior and maybe reflection of that behavior.  And then there's also feedback effects that might occur, so that is the social interaction  within social media platforms that you see.  If you're posting radical content you get more likes and that might increase your own  political convictions in terms of polarization.  Now the first empirical study that I did try to more or less disentangle these different  mechanisms even though I have to admit that that was not possible perfectly with the data  at hand.  What I did was a secondary analysis of the German longitudinal election studies from  the last Bundestagswahl, so we just had an election in Germany a couple of weeks, no,  last week.  Last week this one was the data from the 2017 election and I looked at effective polarization  only here.  So how did evaluation of the own party camp develop over the course of the campaign?  There were I think seven panel waves that, no, six panel waves, waves two to seven that  went into this analysis and it was possible to reconstruct how partisans, so people who  liked a different or one specific party a lot, at the beginning of the campaign, how  their polarization developed over time, over the course of the campaign.  And there were different social media variables included in this data set as well that enabled  me to disentangle effects of passive exposure to political posts, of following party-owned  accounts, of the frequency of sharing political posts, so active user behavior and of commenting,  on the development of effective polarization over time.  And what I found was maybe in with regards to time, I would skip the regression results  here and come to my summary in words.  What I found was that first party evaluations were mostly stable over the course of the  campaign, so this again tells us something about effective polarization in Germany or  maybe in multiparty systems more generally, that this might not even be the most pressing  problem in this kind of system.  And I think the current election results might argue in that direction as well.  That passive exposure to political posts tended to stabilize party evaluation, particularly  so among partisans of right-wing populist AFD and neoliberal FTP, so there were the  strongest social media effects.  And this, in my reasoning, suggests that there were no algorithm-cost polarization effects  at work because we were able to look at selective exposure and active social media use variables  distinctively, so I think the variance that remains to be explained by the general amount  of exposure to political content via social media might be the ones or might be closer  to the actual variance that is being caused by the algorithm.  So this could be interpreted in this direction even though it's still only an approximation  to algorithm effects.  For the following of party-owned accounts, which is selective exposure, and also for  active commenting, we saw an increase of effective polarization, again, only among AFD and FTP  partisans.  For the other parties, partisans, there were literally no effects at all.  So this suggests selective exposure and social media self-effects or social impact effects  being at work here.  Now, for the other direction that I argued we should look into, this is even more at  the initial stage.  This is an experiment that has just been in the field where we looked at the way how different  types of news recommendation systems, which are actually running, influence polarization  at the user level.  So this is data that I just analyzed this week.  This is really preliminary.  It might be that the results look different after maybe including additional covariates  or so.  So what we do here is, this is a large-scale project with colleagues from KIT Karlsruhe.  What we do here is that we use different types of news recommendation systems, content-based  recommendation systems.  So in the first place, recommenders that only look at which texts are similar to each other,  and we want to include user variables in the next steps.  So there will be additional experiments where we also look at demographic recommendation  as the data science colleagues call it, and see how these might affect polarization in  comparison to random recommendations.  So we have a body of articles, about almost 4,000 articles on migration, and we let the  users in four iterations choose from a selection of articles, one article that they are supposed  to read, and this is being repeated four times.  And either these articles, and this is how the surface of the front end looks like for  choosing these articles, and these articles are either randomly picked or they are recommended  by a recommender system, and that is our approximation to the algorithm.  This does not look like Facebook, I know.  But at least content-based recommendation is at work here, and there are not really  many studies that actually do that.  Now very quickly to our results, because I'm running out of time.  In terms of effective polarization, we find that using any kind of recommender system  led to less effective polarization as compared to reading randomly selected articles.  And that suggests at least for content customization, there might even be depolarization effects.  Because the algorithm selects articles for me that are consistent with my convictions,  I might be less polarized as compared to the case where I'm confronted with articles that  are simply randomly selected and therefore might contradict my own opinion stronger.  So there might even be positive news here.  But we also see that recommender systems that have a negative sentiment bias.  So that is of course closer to what Facebook and Twitter do because they consider the emotionality  of posts because these posts receive more interactions, the ones that are more emotionally  charged.  So systems with negative sentiment bias increase effective polarization.  But there is an interaction effect at work here.  This only occurs if people spend more time on the content that they are confronted with,  not if they have been simply clicking through the articles that we confronted them with.  In terms of ideological polarization, we hardly find any effects in this particular study.  The only assumption was that a recommender system with a balanced sentiment tended to  increase ideological polarization.  That might be similar case as above if I'm confronted with content that is not completely  in line with my convictions, then this might lead to polarization.  Now some concluding remarks my last slide.  My impression is, and this is really subjective and personal, that the field suffers from conceptual  heterogeneity and empirical inclusiveness or large empirical gaps as you want to put it.  I believe there are important gaps if I think of the filter bubble question and whether it  has any effects.  In particular with regards to the isolation of algorithm cost polarization effects because  that argument is really strongly emphasized in public debate.  But I think we are still very much picking in the dark here.  And also gaps concerning the effects of selective exposure in active social media use, which  is a bit in the background of the current debate, I think.  My impression is that in the bigger picture algorithmic content selection rather seems  to stabilize or depolarize partisans if you isolate only the effect of algorithmic content  selection and that observable social media effects might, on polarization, might not  mainly be caused by the algorithms and the platform architectures, but rather by user's  behavior on the platforms and with the platforms and that, of course, also includes the leaders,  as you called them, Steven.  So the users that have a lot of followers and that are large-scale content producers,  of course.  But I admit, as I said a couple of times already, there is not enough empirical research to  really be sure about those conclusions.  So this is only my first impression.  Thanks a lot for listening.  I hope that I took a bit too much time.
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Maybe I should first introduce myself. I'm a scholar of media and communication studies at the University of Mannheim. So my main focus is on political journalism and how it shapes attitudes and behaviors. I did a lot of research on political populism in communication, but this is not what I'm going to talk about today. But rather I will be focusing on a new line of thinking that keeps me busy in recent months or years, which is the social media polarized. And my focus since I am a communication researcher is mainly on the filter bubble hypothesis, which is very prominent in our field. So I think this is not exactly what Stephen talked about. This is where the complementarity is. Sorry about that. That should have been animated differently. So I don't have to... Yeah? Did they just miss the last name? No, I don't have to. I don't have to type a gun. I don't think I have to talk a lot about this anymore because we already heard this in the keynote talk this morning. There is an empirical observation that especially effective polarization, as you said, and already showed us with some data, is increasing, not in all countries, mainly in the US. Maybe that is a little bit of a limitation also to my research because my empirical research is based on German data. And we can, of course, and this is maybe a point of discussion that I will come back to in the end, question whether that is actually a most important aspect for German political communication, whether there are polarization effects or not. But we can leave this open for the moment. Now, of course, the question is, and that is also the intention of the track that this talk has been placed by the organizers, what are the roots of this increase of polarization? And there are a couple of, I would say, lines of discussion in the literature. And I'm sticking pretty close to the literature from the field of political psychology, I would say, which is very dominant also in my discipline when it comes to arguing about polarization. And there I see three lines of reasoning, social identity-based explanations, ideology-based explanations, and more or less information exposure or information behavior-based explanations. And that is, of course, where the whole filter bubble argument is situated. So the idea is that social media might be driving polarization, and that is, of course, also an idea which is discussed a lot in public discourse on polarization. So we see a lot of newspaper coverage on the question of how platforms might have contributed to the development of polarization in our societies, and also a lot of non-academic societal discourse in institutions in the political sphere on the road of platforms here. And I think one very famous argument, and I'm very happy that this was not central to Stephen's talk, is the filter bubble hypothesis developed by Eli Perizer, a journalism scholar from New York. And he says that algorithmic content selection is based on users' political preferences, and because of that, the algorithm will show users what they are already interested in. And that means that partisanship consistent information environments might develop. And the argument is this does not stay without any effects. And the effect that is assumed is that partisan ties will increase further, and effective polarization will be promoted by algorithmically shaped content selection. Now the first problem is, the first empirical problem is that, of course, it's very difficult to study these kinds of questions. I think Stephen made that clear already. But there are some studies which have been trying to figure out whether filter bubbles actually occur. And I mean, they are quite heterogeneous in terms of their methodological approach. So it's really difficult to draw general conclusions from these studies. But I think as far as the evidence that I know goes, there is no really convincing evidence that algorithmic selection really creates those filter bubbles. So that people are really more and more confronted with only like-minded views if they are using algorithmic content selection systems. But I admit that this is a very open question. We are in an urgent need of more research about that. And now what I did, myself, together with a PhD student, Katarina Ludwig, is first a systematic lit review. And I think there might, there will probably be a lot of overlap, at least in the bodies of studies that we looked at. I mean, our question was a bit different than yours. We did not only focus on causal effects, but we said we focused on all studies that include social media use as an input variable, and then either effective or ideological polarization as it's usually operationalized in the political psychology literature as the dependent variable. And yeah, but we also looked at studies using cross-sectional data. So it's a bit of a different approach. And as I understood at your lit review, is also about other dependent variables. So it's a bit different in terms of its approach. And I think most findings are somewhat similar. This is also in preparation. The manuscript is not finished yet, so we are not able to submit it at this moment, but we are working on it. Now what are our first insights? First, my impression, at least, is if you use a broad understanding of polarization and look for empirical studies, then you find that the research landscape is to some degree conceptually blurry because studies, maybe that's a bit harsh, tend to an idiosyncratic understanding of polarization, but there are many different ways of conceptually understanding polarization and also empirically measuring polarization out there. So concise conclusions are, in my impression, difficult to draw. The body of literature that we ended up with were 88 empirical studies. We also had some hundreds in the first round of sampling, but we narrowed that down to 88 empirical studies that have been published and only 31 studies we found did investigate polarization effects of social media at the user level. Even though I admit we have to be careful about the term effects because it's not only causal evidence in the narrow sense that Steven's literature review deals with. Now we find that the most frequent ways of operationalizing social media use in that context is that either studies use survey measures of the amount of social media use, which is a very coarse measurement, I think, because it does not really tell us what people are confronted with when turning to social media and also does not tell us anything about what people do with social media. Then we also find a lot of experiments which actually look at the effects of pro-encounter attitudinal information exposure. And social media, in that respect, is only like framework. So they use stimuli, oftentimes also non-interactive stimuli, that have a Facebook logo at the top, but the same experiment could have been conducted without any social media framework around it. So we can question whether these effects that are observed in such studies are actually social media effects or not simply information exposure effects. And we found, because this was our specific focus, and we looked for that, only five studies investigating effects or relationships of using recommender systems with polarization. So this is really an area that is very central to the public debate, but is hardly being studied empirically, probably because it's so difficult to study it. Now which patterns of results did we find? First of all, my or our impression was that there is a heterogeneous landscape of evidence on all questions. So there are studies finding polarization effects. There are also studies finding depolarizing effects. And also null findings studies, or at least null findings on specific hypotheses within those studies. So it's not that you could really say there is one central line of findings that is really clear. Also our impression was, or our finding was that the occurrence of any social media effects is often moderated by the strength of partisan ties and also other political attitude or political attitudinal variables. So that means that not all users, and that is actually to psychologists of course pretty self-evident, not all users are affected by social media in the same way. Also we found that there is a lack of research from multi-party systems, which is our justification of course then to look at the German case empirically, but at the same time that could turn into a boomerang if we admit that there's simply more polarization going on in two-party systems like the US. So yeah, it's a bit ambiguous to stress this point I guess. Particularly with regards to effective polarizations, there is more research on ideological polarization from multi-party systems. Our most stable finding was that social media users are more ideologically polarized and that is something that has been shown for different party systems, but this is in most studies not a cause of finding but a correlation of finding. So it's unclear where the causality is. In terms of effective polarization, our impression was that it's not unequivocally, there is not one clear direction of effects in the studies in terms of polarization, but there are studies that establish a causal relationship between social media use and effective polarizations and also studies that fail to do so. So we do not see clear evidence here. Also no clear evidence for recommender system effects. So the filter bubble hypothesis is far from being supported at the moment, but at the same time I have to say there is simply not enough substantial empirical evidence to draw a conclusion whether that is actually an effect of lack of evidence or a substantial effect. Now for the whole filter bubble debate I see two lines of or two major tasks that we have to engage in from that point. The first one is analyze algorithm and other social media effects on polarization competitively. So my notion always is that the algorithm is not all that social media is about, but there is a lot of user behavior going on on social media. Users are tailoring their information environments by themselves and not only the algorithm does that, so there are other things going on in social media use that are not really connected to the algorithm and we have to look at them competitively. And the other thing is that I think we need to analytically isolate algorithm effects that is really not look at social media use in general as a predictor of polarization but really try to figure out what is the algorithm doing to users and explore maybe also different ways of how an algorithm could work and select content for users in more depth. Now I would like to, and this has a little bit of a workshop character because these are two studies that have not been published yet just as the lit review. I would like to present you some preliminary results of studies that I did going in those two directions. The first one was on the competitive assessment of social media effects of different patterns of users behavior and for that, oh no, first what have been my conceptual ideas about that. So that is still very coarse but I think it's more fine-grained than what much research offers us for ideas on how social media might affect polarization. The first thing is the infamous filter bubble hypothesis, so effects of algorithmic content selection that lead to content environments that are really in line with people's political attitudes. Then there is a second effect and that context of passive content exposure via social media that could occur and that is selective exposure. So users are choosing, are actively selecting which accounts to follow, which friends they have and that might also contribute to the like-minded information environment. In some parts of the literature this is referred to as the echo chamber hypothesis. But then there is also active social media use and I think that might be very important for psychologists particularly because also active user behavior on social media might contribute to the shaping of polarization and two ideas I've got there, maybe there are more than that, is social media self-effects. So the argument that if you're posting about something, that you're giving some political content alike, that because of consistency of our strife for internal consistency might increase us or might strengthen us in our own beliefs and that is a hypothesis of self-radicalization through behavior and maybe reflection of that behavior. And then there's also feedback effects that might occur, so that is the social interaction within social media platforms that you see. If you're posting radical content you get more likes and that might increase your own political convictions in terms of polarization. Now the first empirical study that I did try to more or less disentangle these different mechanisms even though I have to admit that that was not possible perfectly with the data at hand. What I did was a secondary analysis of the German longitudinal election studies from the last Bundestagswahl, so we just had an election in Germany a couple of weeks, no, last week. Last week this one was the data from the 2017 election and I looked at effective polarization only here. So how did evaluation of the own party camp develop over the course of the campaign? There were I think seven panel waves that, no, six panel waves, waves two to seven that went into this analysis and it was possible to reconstruct how partisans, so people who liked a different or one specific party a lot, at the beginning of the campaign, how their polarization developed over time, over the course of the campaign. And there were different social media variables included in this data set as well that enabled me to disentangle effects of passive exposure to political posts, of following party-owned accounts, of the frequency of sharing political posts, so active user behavior and of commenting, on the development of effective polarization over time. And what I found was maybe in with regards to time, I would skip the regression results here and come to my summary in words. What I found was that first party evaluations were mostly stable over the course of the campaign, so this again tells us something about effective polarization in Germany or maybe in multiparty systems more generally, that this might not even be the most pressing problem in this kind of system. And I think the current election results might argue in that direction as well. That passive exposure to political posts tended to stabilize party evaluation, particularly so among partisans of right-wing populist AFD and neoliberal FTP, so there were the strongest social media effects. And this, in my reasoning, suggests that there were no algorithm-cost polarization effects at work because we were able to look at selective exposure and active social media use variables distinctively, so I think the variance that remains to be explained by the general amount of exposure to political content via social media might be the ones or might be closer to the actual variance that is being caused by the algorithm. So this could be interpreted in this direction even though it's still only an approximation to algorithm effects. For the following of party-owned accounts, which is selective exposure, and also for active commenting, we saw an increase of effective polarization, again, only among AFD and FTP partisans. For the other parties, partisans, there were literally no effects at all. So this suggests selective exposure and social media self-effects or social impact effects being at work here. Now, for the other direction that I argued we should look into, this is even more at the initial stage. This is an experiment that has just been in the field where we looked at the way how different types of news recommendation systems, which are actually running, influence polarization at the user level. So this is data that I just analyzed this week. This is really preliminary. It might be that the results look different after maybe including additional covariates or so. So what we do here is, this is a large-scale project with colleagues from KIT Karlsruhe. What we do here is that we use different types of news recommendation systems, content-based recommendation systems. So in the first place, recommenders that only look at which texts are similar to each other, and we want to include user variables in the next steps. So there will be additional experiments where we also look at demographic recommendation as the data science colleagues call it, and see how these might affect polarization in comparison to random recommendations. So we have a body of articles, about almost 4,000 articles on migration, and we let the users in four iterations choose from a selection of articles, one article that they are supposed to read, and this is being repeated four times. And either these articles, and this is how the surface of the front end looks like for choosing these articles, and these articles are either randomly picked or they are recommended by a recommender system, and that is our approximation to the algorithm. This does not look like Facebook, I know. But at least content-based recommendation is at work here, and there are not really many studies that actually do that. Now very quickly to our results, because I'm running out of time. In terms of effective polarization, we find that using any kind of recommender system led to less effective polarization as compared to reading randomly selected articles. And that suggests at least for content customization, there might even be depolarization effects. Because the algorithm selects articles for me that are consistent with my convictions, I might be less polarized as compared to the case where I'm confronted with articles that are simply randomly selected and therefore might contradict my own opinion stronger. So there might even be positive news here. But we also see that recommender systems that have a negative sentiment bias. So that is of course closer to what Facebook and Twitter do because they consider the emotionality of posts because these posts receive more interactions, the ones that are more emotionally charged. So systems with negative sentiment bias increase effective polarization. But there is an interaction effect at work here. This only occurs if people spend more time on the content that they are confronted with, not if they have been simply clicking through the articles that we confronted them with. In terms of ideological polarization, we hardly find any effects in this particular study. The only assumption was that a recommender system with a balanced sentiment tended to increase ideological polarization. That might be similar case as above if I'm confronted with content that is not completely in line with my convictions, then this might lead to polarization. Now some concluding remarks my last slide. My impression is, and this is really subjective and personal, that the field suffers from conceptual heterogeneity and empirical inclusiveness or large empirical gaps as you want to put it. I believe there are important gaps if I think of the filter bubble question and whether it has any effects. In particular with regards to the isolation of algorithm cost polarization effects because that argument is really strongly emphasized in public debate. But I think we are still very much picking in the dark here. And also gaps concerning the effects of selective exposure in active social media use, which is a bit in the background of the current debate, I think. My impression is that in the bigger picture algorithmic content selection rather seems to stabilize or depolarize partisans if you isolate only the effect of algorithmic content selection and that observable social media effects might, on polarization, might not mainly be caused by the algorithms and the platform architectures, but rather by user's behavior on the platforms and with the platforms and that, of course, also includes the leaders, as you called them, Steven. So the users that have a lot of followers and that are large-scale content producers, of course. But I admit, as I said a couple of times already, there is not enough empirical research to really be sure about those conclusions. So this is only my first impression. Thanks a lot for listening. I hope that I took a bit too much time.
the end, question whether that is actually a most be driving polarization, and that is, of course, Ludwig, is first a systematic lit review. And I investigate polarization effects of social media at the user level. Even though I admit we have to find that the most frequent ways of questions. So there are studies finding the occurrence of any social media effects is research on ideological polarization from that social media users are more ideologically the filter bubble hypothesis is far from being substantial empirical evidence to draw a and other social media effects on polarization might affect polarization. The first thing is the infamous filter bubble hypothesis, so effects of algorithmic content selection that lead to second effect and that context of passive content user behavior on social media might contribute to variables included in this data set as well that algorithm-cost polarization effects at work bit in the background of the current debate, I be caused by the algorithms and the platform followers and that are large-scale content