Cart (Loading....) | Create Account
Close category search window
 

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

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.

Published in:

Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom)

Date of Conference:

9-11 Oct. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.