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Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter

Naveed, Nasir and Gottron, Thomas and Kunegis, Jérôme and Alhadi, Arifah Che (2011) Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter. pp. 1-7. In: Proceedings of the ACM WebSci'11, June 14-17 2011, Koblenz, Germany.

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Official URL: http://www.websci11.org/fileadmin/websci/Papers/50_paper.pdf

Abstract

On the microblogging site Twitter, users can forward any message they receive to all of their followers. This is called a retweet and is usually done when users find a message particularly interesting and worth sharing with others. Thus, retweets reflect what the Twitter community considers interesting on a global scale, and can be used as a function of interestingness to generate a model to describe the content-based characteristics of retweets. In this paper, we analyze a set of high- and low-level content-based features on several large collections of Twitter messages. We train a prediction model to forecast for a given tweet its likelihood of being retweeted based on its contents. From the parameters learned by the model we deduce what are the influential content features that contribute to the likelihood of a retweet. As a result we obtain insights into what makes a message on Twitter worth retweeting and, thus, interesting.

Item Type:Conference or Workshop Item (Paper)
Web Science Comments:WebSci Conference 2011
Subjects:WS2 Artificial Intelligence > WS22 Languages
Web Science Events > Web Science 2011
ID Code:435
Deposited By: Lisa Sugiura
Deposited On:07 Jun 2011 16:54
Last Modified:25 Oct 2011 17:11

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