He says, she says: conflict and coordination in Wikipedia
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Local and global algorithms for disambiguation to Wikipedia
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The Filter Bubble: What the Internet Is Hiding from You
The Filter Bubble: What the Internet Is Hiding from You
Harmony and dissonance: organizing the people's voices on political controversies
Proceedings of the fifth ACM international conference on Web search and data mining
Identifying controversial issues and their sub-topics in news articles
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
MOUNA: mining opinions to unveil neglected arguments
Proceedings of the 21st ACM international conference on Information and knowledge management
Identifying controversial articles in Wikipedia: a comparative study
Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration
Sentiment diversification with different biases
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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A useful feature to facilitate critical literacy would alert users when they are reading a controversial web page. This requires solving a binary classification problem: does a given web page discuss a controversial topic? We explore the feasibility of solving the problem by treating it as supervised k-nearest-neighbor classification. Our approach (1) maps a webpage to a set of neighboring Wikipedia articles which were labeled on a controversiality metric; (2) coalesces those labels into an estimate of the webpage's controversiality; and finally (3) converts the estimate to a binary value using a threshold. We demonstrate the applicability of our approach by validating it on a set of webpages drawn from seed queries. We show absolute gains of 22% in F_0.5 on our test set over a sentiment-based approach, highlighting that detecting controversy is more complex than simply detecting opinions.