Re-tweeting from a linguistic perspective
LSM '12 Proceedings of the Second Workshop on Language in Social Media
Quality models for microblog retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Temporal models for microblogs
Proceedings of the 21st ACM international conference on Information and knowledge management
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
An investigation on repost activity prediction for social media events
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
On the prediction of re-tweeting activities in social networks: a report on WISE 2012 challenge
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
Prediction in a microblog hybrid network using bonacich potential
Proceedings of the 7th ACM international conference on Web search and data mining
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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.