Introduction to Information Retrieval
Introduction to Information Retrieval
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
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
IMASS: an intelligent microblog analysis and summarization system
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Modeling and Visualizing Information Propagation in a Micro-blogging Platform
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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Social media present a user-friendly way of communication and sharing, which brings new chances to understand users and their social communication patterns. With the popularity of microblogging services, the huge volume of very short texts makes it difficult to track the latest updates or breaking news. In this paper, we propose a novel social influence model for estimating the popularity score for each short text in plurk. First, the degrees of user participation and user propagation are estimated by the number of replies, replurks, likes, and URIs. Then, we measure the influence persistence by the duration of the initial post and the last response, and the influence score can be derived from a linear combination of these simple statistics. Our experimental results on more than 300 thousand plurks collected from 1,750 users showed a good performance in determining popular messages, with the best F-measure of 0.86. From our case studies, top-ranked messages can accurately reflect the popular discussions on important events. This shows the effectiveness of our proposed approach. Further investigation of applying the influence model in event detection is needed.