A Analyzing the Adoption and Cascading Process of OSN-based Gifting Applications: An Empirical Study 1 M. Rezaur Rahman , Department of Computer Science, University of California, Davis Jinyoung Han , Department of Electrical and Computer Engineering, University of California, Davis Yong Jae Lee , Department of Computer Science, University of California, Davis Chen-Nee Chuah , Department of Electrical and Computer Engineering, University of California, Davis To achieve growth in user base of OSN-based applications, word-of-mouth diffusion mechanisms such as user-to-user invitations are widely used. This paper characterizes the adoption and cascading process of OSN-based applications that grow via user invitations. We analyze a detailed large-scale dataset of a popular Facebook gifting application, iHeart, that contains more than 2 billion entries of user activities generated by 190 million users during a span of 64 weeks. We investigate: (1) how users invite their friends to an OSN-based application, (2) how application adoption of an individual user can be predicted, (3) what factors drive the cascading process of application adoptions, and (4) what are the good predictors of the ultimate cascade sizes. We find that sending or receiving a large number of invitations does not necessarily help to recruit new users to iHeart. We also find that the average success ratio of inviters is the most important feature in predicting an adoption of an individual user, which indicates that the effectiveness of inviters has strong predictive power with respect to application adoption. Based on the lessons learned from our analyses, we build and evaluate learning-based models to predict whether a user will adopt iHeart or not. Our proposed model that utilizes additional activity information of individual users from other similar type of gifting applications can achieve high precision (83%) in predicting adoptions in the target application (i.e., iHeart). We next identify a set of distinctive features that are good predictors of the growth of the application adoptions in terms of final population size. We finally propose a prediction model to infer whether a cascade of application adoption will continue to grow in the future based on observing the initial adoption process. Results show that our proposed model can achieve high precision (over 80%) in predicting large cascades of application adoptions. We believe our work can give an important implication in resource allocation of OSN-based product stakeholders, e.g., via targeted marketing. 1. INTRODUCTION The word-of-mouth information diffusion [Rodrigues et al. 2011] over online social net- works (OSNs) has been regarded as an important mechanism by which people discover and share a new idea, technology, content, or product. Such diffusion mechanism has gained great attentions not only from research communities but also from enterprises interested in growing their business. According to the New York Times article, tradi- tional retailers like Target and Walmart have sought partners in OSN companies [Har- ris 2013]. The growing interest in such information diffusion mechanism has attracted the re- search community to investigate how information (e.g., a photo, link, or product) are reshared in OSNs like Facebook, Twitter, Flickr, or Pinterest [Cheng et al. 2014; Cha et al. 2009; Kwak et al. 2010; Rodrigues et al. 2011; Han et al. 2014]. A news, photo, link, or product may get reshared (e.g., retweet in Twitter or repin in Pinterest) multi- ple times over the OSNs, hence generating a cascade that potentially reaches a large number of users. These studies have revealed valuable insights into the macro-level propagation patterns of messages [Kwak et al. 2010], URLs [Rodrigues et al. 2011], news [Lerman and Ghosh 2010], and photos [Cha et al. 2009; Han et al. 2014]. How- ever, most of these studies paid little attention to how user recommendation of products leads to their actual adoption and further propagation. This is the key to understand- ing and predicting the growth, popularity, and longevity of OSN-based products and/or the ultimate size of the user base. 1 This paper is an extended version of our earlier work [Rahman et al. 2015]. ACM Transactions on the Web, Vol. V, No. N, Article A, Publication date: January YYYY.
['M. Rezaur Rahman', 'Jinyoung Han', 'Yong Jae Lee', 'Chen-Nee Chuah']
Analyzing the Adoption and Cascading Process of OSN-based Gifting Applications: An Empirical Study