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The Utility of Social and Topical Factors in Anticipating Repliers in Twitter Conversations

By Johannes Schantl, Claudia Wagner, Rene Kaiser and Markus Strohmaier


Anticipating repliers in online conversations is a fundamental challenge for computer mediated communication systems which aim to make textual, audio and/or video communication as natural as face to face communication. The massive amounts of data that social media generates has facilitated the study of online conversations on a scale unimaginable a few years ago. In this work we use data from Twitter to explore the predictability of repliers, and investigate the factors which influence who will reply to a message. Our results suggest that social factors, which describe the strength of relations between users, are more useful than topical factors. This indicates that Twitter users ’ reply behavior is more impacted by social relations than by topics. Finally, we show that a binary classification model, which differentiates between users who will and users who will not reply to a certain message, may achieve an F1-score of 0.74 when using social features. Author Keywords Twitter, social media communication, reply behavior, reply predictio

Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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