Social Influence Locality for Modeling Retweeting Behaviors

We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users' behaviors are mainly influenced by close friends in their ego networks. We provide a formal definition for the notion of social influence locality and develop two instantiation functions based on pairwise influence and structural diversity. The defined influence locality functions have strong predictive power. Without any additional features, we can obtain a F1-score of 71.65% for predicting users' retweet behaviors by training a logistic regression classifier based on the defined functions. Our analysis also reveals several intriguing discoveries. For example, though the probability of a user retweeting a microblog is positively correlated with the number of friends who have retweeted the microblog, it is surprisingly negatively correlated with the number of connected circles that are formed by those friends.

[1]  Jimeng Sun,et al.  Neighborhood formation and anomaly detection in bipartite graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[2]  L. Asz.  Random Walks on Graphs: a Survey , 2022 .

[3]  Gregor Heinrich.  Parameter estimation for text analysis , 2009 .

[4]  Jie Tang,et al.  Modeling Indirect Influence on Twitter , 2012, Int. J. Semantic Web Inf. Syst..

[5]  Harry Shum,et al.  An Empirical Study on Learning to Rank of Tweets , 2010, COLING.

[6]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[7]  Jiawei Han,et al.  Learning influence from heterogeneous social networks , 2012, Data Mining and Knowledge Discovery.

[8]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[9]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[10]  Yong Yu,et al.  Collaborative personalized tweet recommendation , 2012, SIGIR '12.

[11]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[12]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[13]  Lars Backstrom,et al.  Structural diversity in social contagion , 2012, Proceedings of the National Academy of Sciences.

[14]  Masahiro Kimura,et al.  Prediction of Information Diffusion Probabilities for Independent Cascade Model , 2008, KES.

[15]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[16]  Conor Hayes,et al.  Cross-Community Influence in Discussion Fora , 2012, ICWSM.

[17]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[18]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[19]  Juan-Zi Li,et al.  Understanding retweeting behaviors in social networks , 2010, CIKM.

[20]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[21]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.