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Finding Elite Voters in Daum View: Using Media Credibility Measures

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6045))

Abstract

As news media have been expected to provide valuable news contents to readers, credibility of each medium depends on what news contents it has created and delivered. In traditional news media, staff editors look into news articles and arrange news contents to enhance their media credibility. By contrast, in social news services, general users play an important role in selecting news contents through voting behavior as it is practically impossible for staff editors to go through thousands of articles sent to the services. However, although social news services have strived to develop news ranking systems that select valuable news contents utilizing users’ participation, they still represent popularity rather than credibility, or give users too much burden.

In this paper, we examined whether there is a group of elite users who votes for articles whose journalistic values are higher than others. To do this, we firstly assessed journalistic values of 100 social news contents with a survey. Then, we extracted a group of elite users based on what articles they had voted for. To prove that the elite group shows a tendency to vote for journalistically valuable news contents, we analyzed their voting behavior in another news pool. Finally, we concluded with a promising result that news contents voted by the elite users show significantly higher credibility scores than other news stories do while the number of votes from general users is not significantly correlated with the scores.

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© 2010 Springer-Verlag Berlin Heidelberg

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Kim, K., Park, H., Ko, J., Kim, Yr., Han, S.S. (2010). Finding Elite Voters in Daum View: Using Media Credibility Measures. In: Breslin, J.G., Burg, T.N., Kim, HG., Raftery, T., Schmidt, JH. (eds) Recent Trends and Developments in Social Software. BlogTalk BlogTalk 2008 2009. Lecture Notes in Computer Science, vol 6045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16581-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-16581-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16580-1

  • Online ISBN: 978-3-642-16581-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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