Difference between revisions of "Park et al CSCW 2011. The Politics of Comments: Predicting Political Orientation of News Stories with Commenters’ Sentiment Patterns"
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The paper does several evaluations of whether the comenter's political preference can clearly be identified from their comments. | The paper does several evaluations of whether the comenter's political preference can clearly be identified from their comments. | ||
| + | ==== Consistency of Political Orientation ==== | ||
| + | Analysis of 100 active (on both the set, Popular or General) commenters were done. Both the article sets covered major political issues. 20 recent comments were sampled from each commenter. A commenter was considered to show consistency when the political position expressed in all comment samples is consistent. Those who changed their position for at least once were tagged as “vague”. Figure below shows the result. | ||
| − | + | [[File:consistency.jpg]] | |
Revision as of 21:12, 1 October 2012
Contents
Citation
Souneil Park, Minsam Ko, Jungwoo Kim, Ying Liu, and Junehwa Song.“The Politics of Comments: Predicting Political Orientation of News Stories with Commenters’ Sentiment Patterns”, in Proceedings of the 2011 ACM Conference on Computer Supported Cooperative Work (CSCW 2011).
Online Version
Summary
This Paper tries to predict the political orientation of news articles by analyzing the sentiment patterns of commenters. It is difficult to interpret the political orientation of a news article by computation analysis of the text or metadata since they cover complex political discourse such as party, government, economy etc. This paper presents a new "social annotation analysis" approach of predicting the political orientation of news articles.
The main idea
Though it is a difficult problem to analyze the political orientation of a news article by computational analysis, however there exists commenters with clear political views and they are most likely to present the same views consistently towards various political issues. By identifying predictive commenters (who show a high degree of regularity in their sentiment patterns) and analyzing their sentiments of comments, the political orientation of the news article is deduced. When the comment is negative, the article’s political orientation can be predicted to be the opposite from that of the commenters; when the comment is positive, it can be predicted to be the same as that of the commenter.
Data and Analysis
An extensive study is conducted by choosing commenters and their comment history from Naver News, a popular Internet news portal in South Korea. The study meet the prerequisites of their assumption
- Existence of active commenters who continuously comment on a large amount of articles.
- Most of them have a clear political preference either as liberal or conservative
- Among them, there are predictive commenters.
Commenters from two article sets with different characterestics were sampled. The Popular Set is composed of a collection of the 20 most read political news articles of the day for a 6 month period. As the stories are popular, they have many comments. The General Set is sampled from the Naver political issue directory. The articles were sampled from major political issues that were updated from 2008.12 to 2009.11. The set includes both articles with many comments and those with few. The Naver ID's were designated as the identifiers of the commenters. Only the top level comments were considered. The figure below shows the data used.
Commenter's Political Orientation
The paper does several evaluations of whether the comenter's political preference can clearly be identified from their comments.
Consistency of Political Orientation
Analysis of 100 active (on both the set, Popular or General) commenters were done. Both the article sets covered major political issues. 20 recent comments were sampled from each commenter. A commenter was considered to show consistency when the political position expressed in all comment samples is consistent. Those who changed their position for at least once were tagged as “vague”. Figure below shows the result.
