Social Media News Communities: Gatekeeping, Coverage, and Statement Bias
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We examine biases in online news sources and social media communities around them. To that end, we introduce unsupervised methods considering three types of biases: selection or "gatekeeping" bias, coverage bias, and statement bias, characterizing each one through a series of metrics. Our results, obtained by analyzing 80 international news sources during a two-week period, show that biases are subtle but observable, and follow geographical boundaries more closely than political ones. We also demonstrate how these biases are to some extent ampli ed by social media.
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Social Media News Communities: Gatekeeping, Coverage, and Statement Bias
- 1. Social Media News Communities: Gatekeeping, Coverage, and Statement Bias Diego Saez-Trumper∗1 Carlos Castillo† Mounia Lalmas‡ ∗ Universitat † Qatar Pompeu Fabra, Barcelona Computing Research Institute, Doha ‡ Yahoo Labs London San Francisco, October, 2013 1 This work was done while visiting the Qatar Computing Research Institute
- 2. ”Media bias refers to (...) the selection of which stories are reported and how they are reported”. S. Rivolta
- 4. Selection
- 7. Goal: quantify biases present in online news
- 8. Challenges Consider a large set of news sources. Compare news sources with social media (Twitter). Use unsupervised methods.
- 9. Data set - News Sources Use the top-100 news websites from Alexa.com. Download all the news they publish trough RSS and Twitter.
- 10. Data set - Twitter Download all tweets containing a URL pointing to a news source. Community = Followers People who have tweeted at least K1 articles from a given news source within K2 days.
- 11. Selection Bias (Gatekeeping) Compute similarity among news sources using the Jaccard coefficient. Project it in two dimensions using PCA.
- 12. Selection Bias (Gatekeeping) News Sources Twitter Geographical pattern No clear pattern.
- 13. Coverage Compute similarity among news sources using the Jensen-Shannon divergence (JS) . Coverage Bias(s1 , s2 ) = 1 − JS(s1 , s2 )
- 14. Coverage Bias News Sources Stronger geographical pattern.
- 16. Political leaning News Sources Twitter Stronger political leaning signal in Twitter.
- 17. Statement Use sentiment analyses to find positive/negative sentiments associated to a person.
- 18. Statement Obama Thatcher Sentiments are more extreme in Twitter.
- 19. Conclusions Strong geographical patterns. Political leaning signal is stronger in Social Media. Feelings are more extreme in Social Media.