Difference between revisions of "Popescu and Pennacchiotti, CIKM 10"
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This paper addresses the task of identifying controversial events using Twitter as a starting point. | This paper addresses the task of identifying controversial events using Twitter as a starting point. | ||
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| + | 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). | ||
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| + | 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. | ||
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| + | They make extensive use of lexicons such as | ||
| + | 1. Controversy lexicon derived from Wikipedia's controversial topic list | ||
| + | 2. Bad words lexicons [http://urbanoalvarez.es/blog/2008/04/04/bad-words-list/] | ||
== Evaluation == | == Evaluation == | ||
Revision as of 22:46, 30 September 2012
This Paper is relevant to our project on detecting controversial events in Twitter.
Contents
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.
- A Statistical Model for Popular Events Tracking in Social Communities. Lin et al, KDD 2011 This paper address a method to observe and track the popular events or topics that evolve over time in the communities.
- A study on retrospective and online event detection. Yang et al, SIGIR 98 This paper addresses the problems of detecting events in news stories.
- Temporal and information flow based event detection from social text streams. Zhao et al, AAAI 07 This paper addresses the problems of detecting events in news stories.
- Automatic Detection and Classification of Social Events. Agarwal and Rambow, ACL 10 This paper aims at detecting and classifying social events using Tree kernels.
- Information credibility on twitter. Castillo et al, WWW 11 The authors develop a general approach to change-point detection that generalize across wide range of application.
