2012 MiningContentionsfromDiscussion

• (Mukherjee & Liu, 2012) ⇒ Arjun Mukherjee, and Bing Liu. (2012). “Mining Contentions from Discussions and Debates.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339664

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Mining+Contentions+from+Discussions+and+Debates
• http://dl.acm.org/citation.cfm?id=2339530.2339664&preflayout=flat#citedby

Quotes

Author Keywords

• Content analysis and indexing; contention analysis; data mining; debates; discussions; social media

Abstract

Social media has become a major source of information for many applications. Numerous techniques have been proposed to analyze network structures and text contents. In this paper, we focus on fine-grained mining of contentions in discussion / debate forums. Contentions are perhaps the most important feature of forums that discuss social, political and religious issues. Our goal is to discover contention and agreement indicator expressions, and contention points or topics both at the discussion collection level and also at each individual post level. To the best of our knowledge, limited work has been done on such detailed analysis. This paper proposes three models to solve the problem, which not only model both contention / agreement expressions and discussion topics, but also, more importantly, model the intrinsic nature of discussions / debates, i.e., interactions among discussants or debaters and topic sharing among posts through quoting and replying relations. Evaluation results using real-life discussion / debate posts from several domains demonstrate the effectiveness of the proposed models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MiningContentionsfromDiscussion	Bing Liu Arjun Mukherjee			Mining Contentions from Discussions and Debates				10.1145/2339530.2339664		2012