Date of Original Version

2013

Type

Conference Proceeding

Rights Management

© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published at http://doi.acm.org/10.1145/2492517.2492554

Abstract or Description

In political contexts, it is known that people act as "motivated reasoners", i.e., information is evaluated first for emotional affect, and this emotional reaction influences later deliberative reasoning steps. As social media becomes a more and more prevalent way of receiving political information, it becomes important to understand more completely the interaction between information, emotion, social community, and information-sharing behavior. In this paper, we describe a high-precision classifier for politically-oriented tweets, and an accurate classifier of a Twitter user's political affiliation. Coupled with existing sentiment-analysis tools for microblogs, these methods enable us to systematically study the interaction of emotion and sharing in a large corpus of politically-oriented microblog messages, collected from just before the 2012 US presidential election. In particular, we seek to understand how information sharing is influenced by the political affiliation of the sender and receiver of a message, and the sentiment associated with the message.

DOI

10.1145/2492517.2492554

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Published In

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 282-289.