The Journal of Machine Learning Research
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Beyond Microblogging: Conversation and Collaboration via Twitter
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
A Comparative Study of Utilizing Topic Models for Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Discovering User Interest on Twitter with a Modified Author-Topic Model
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
User oriented tweet ranking: a filtering approach to microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
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This paper provides a deep analysis of user retweet behavior on Twitter. While previous works about analyzing retweet have mainly focused on predicting the retweetability of each tweet, they lacked interpretations at an individual level. In this paper, we perform a general analysis of retweet behavior from the perspective of individual users. Specifically, we train a prediction model to forecast whether a tweet will be retweeted by a given user, leveraging four different types of features: social-based, content-based, tweet-based and author-based features. By performing """"leave-one-feature-out"""" comparisons, we identify factors that are strongly associated with user retweet behavior.