Retweet Prediction based on Topic, Emotion and Personality

Social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. To understand how information is diffused in these social networks, it is important to examine users’ online activities and behaviors. In this work, we focus on Twitter and study the impact of users’ behaviors on their retweet activities (the major way of information diffusion on Twitter). We consider the topic preference, emotion and personality of a user as part of the user profile to represent their online behavior. The user profile can be built based on all their past tweets, retweets, or both. We propose two types of retweet prediction models, one is using classification algorithms, and the other is using matrix factorization algorithms. In the matrix factorization approach, we include behavior features into the basic factorization model through newly defined regularization terms. The experimental results show that in terms of the F1-score, our classification models based on user behavior related features provided 5%-9% improvement over baseline models and the matrix factorization model showed 4%-6% improvement over the baseline. We also find that by only considering the retweets, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweets and tweets are included.