Vocal Minority Versus Silent Majority: Discovering the Opionions of the Long Tail

Abstract

Social networks such as Face book and Twitter have become the favorite places on the Web where people discuss real-time events. In fact, search engines such as Google and Bing have special agreements, which allow them to include into their search results public conversations happening in real-time in these social networks. However, for anyone who only reads these conversations occasionally, it is difficult to evaluate the (often) complex context in which these conversation bits are embedded. Who are the people carrying on the conversation? Are they random participants or people with a specific agenda? Making sense of real-time social streams often requires much more information than what is visible in the messages themselves. In this paper, we study this phenomenon in the context of one political event: a special election for the US Senate which took place in Massachusetts in January 2010, as observed in conversations on Twitter. We present results of data analysis that compares two groups of different users: the vocal minority (users who tweet very often) and the silent majority (users who tweeted only once). We discover that the content generated by these two groups is significantly different, therefore, researchers should take care in separating them when trying to create predictive models based on aggregated data.

DOI: 10.1109/PASSAT/SocialCom.2011.188

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Cite this paper

@article{Mustafaraj2011VocalMV, title={Vocal Minority Versus Silent Majority: Discovering the Opionions of the Long Tail}, author={Eni Mustafaraj and Samantha Finn and Carolyn Whitlock and Panagiotis Takis Metaxas}, journal={2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing}, year={2011}, pages={103-110} }