Autoregressive Model for Users’ Retweeting Profiles

Social media has become an important means of everyday communication. It is a mechanism for “sharing” and “resharing” of information. While social network platforms provide the means to users for resharing (aka retweeting), it remains unclear what motivates users to retweet. Previous studies have shown that history of users’ interaction and properties of the message are good attributes to understand the retweet behaviour of users. They however, do not consider the fact that users do not read all the blogs on their site. This results in shortcomings in the models used. We realised that simple feature engineering is also not enough to address this problem. To mitigate this, we propose an incremental model called Influence Time Content (ITC) model for predicting retweeting behavior by considering the fact that users do not read all their tweets. We have tested the effectiveness of this model by using real data from Twitter. In addition, we also investigate the parameters of the model for different classes of users. We found some interesting distinguishing patterns in retweeting behavior of users. Less active users are more topically motivated for retweeting a message than active users, who on the other hand, are social in nature.