Predicting lifespans of popular tweets in microblog

Authors:
Shoubin Kong;Ling Feng;Guozheng Sun;Kan Luo
Affiliations:
Dept. of CS&T, Tsinghua University, Beijing, China;Dept. of CS&T, Tsinghua University, Beijing, China;Tencent, SOSO, Beijing, China;Tencent, SOSO, Beijing, China
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

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Abstract

In microblog like Twitter, popular tweets are usually retweeted by many users. For different tweets, their lifespans (i.e., how long they will stay popular) vary. This paper presents a simple yet effective approach to predict the lifespans of popular tweets based on their static characteristics and dynamic retweeting patterns. For a potentially popular tweet, we generate a time series based on its first-hour retweeting information, and compare it with those of historic tweets of the same author and post time (at the granularity of hour). The top-k historic tweets are identified, whose mean lifespan is estimated as the lifespan of the new tweet. Our experiments on a three-month real data set from Tencent Microblog demonstrate the effectiveness of the approach.