Retweet Modeling Using Conditional Random Fields

TLDR

Twitter’s retweet feature is a prominent means of secondary content promotion, yet the motivations behind retweeting remain unclear. The study proposes to model retweet patterns with conditional random fields using content, network, and temporal decay features. The authors employ CRFs and investigate partitioning social graphs to construct network relations for retweet prediction. Experiments show that incorporating social relationships in CRFs improves retweet prediction over baselines.

Abstract

Among the most popular micro-blogging service, Twitter recently introduced their reblogging service called retweet to allow a user to repopulate another user's content for his followers. It quickly becomes one of the most prominent features on Twitter and an important mean for secondary content promotion. However, it remains unclear what motivates users to retweet and whether the retweeting decisions are predictable based on a user's tweeting history and social relationships. In this paper, we propose modeling the retweet patterns using conditional random fields with a three types of user-tweet features: content influence, network influence and temporal decay factor. We also investigate approaches to partition the social graphs and construct the network relations for retweet prediction. Our experiments demonstrate that CRF can improve prediction effectiveness by incorporating social relationships compared to the baselines that do not.