Popescu and Pennacchiotti, CIKM 10

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This Paper is relevant to our project on detecting controversial events in Twitter.

Detecting controversial events from Twitter

Citation

Ana-Maria Popescu and Marco Pennacchiotti. Detecting controversial events from Twitter. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM ’10, pages 1873–1876, New York, NY, USA, 2010. ACM.

Online version

Detecting controversial events from Twitter

Summary

This paper addresses the task of identifying controversial events using Twitter as a starting point.

Events are defined in relation to a target entity, which is an activity with a clear, finite duration in which the target entity plays a key role. The event is considered controversial if it provokes a public discussion in which audience members express opposing opinions or disbeliefs. This is in contrast to events which have little reaction or are overwhelmingly positive/negative (i.e high entropy in opinions).

The authors seek to extract such snapshots of controversial events by modeling the task as a supervised machine learning problem. Each snapshot is represented by a feature vector constructed from Twitter and other sources (such as news). They present several models for comparison, a direct model using regression, a 2-step pipeline model where one detects events and then measure its controversy, and a 2-step blended model where the results of the event detection step are used as an input to the controversy detection regression model.

They make extensive use of lexicons such as 1. Controversy lexicon derived from Wikipedia's controversial topic list 2. Bad words lexicons [1]

Evaluation

Discussion

Related papers

There has been a lot of work on event detection.

Study plan

  • Article: Adaptive time series model [2]
  • Graph cut based clustering [3]