A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Short text classification in twitter to improve information filtering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
TURank: twitter user ranking based on user-tweet graph analysis
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Efficient filtering in micro-blogging systems: we won't get flooded again
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Tweet recommendation with graph co-ranking
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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
Retweet or not?: personalized tweet re-ranking
Proceedings of the sixth ACM international conference on Web search and data mining
A survey of recommender systems in twitter
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Personal User or Organizational User? Behavior on Microblog can Tell
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Analyzing User Retweet Behavior on Twitter
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Network activity feed: finding needles in a haystack
Proceedings of the 4th International Workshop on Modeling Social Media
Analyzing and predicting viral tweets
Proceedings of the 22nd international conference on World Wide Web companion
A framework for detecting public health trends with Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Predicting retweet count using visual cues
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Are Some Tweets More Interesting Than Others? #HardQuestion
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
Identifying interesting Twitter contents using topical analysis
Expert Systems with Applications: An International Journal
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The increasing volume of streaming data on microblogs has re-introduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value. In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how retweet likelihood correlates with the interestingness of the tweets.