Sarcasm detection on twitter: A behavioral modeling approach

Ashwin Rajadesingan, Reza Zafarani, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

118 Scopus citations

Abstract

Sarcasm is a nuanced form of language in which individuals state the opposite of what is implied. With this intentional ambiguity, sarcasm detection has always been a challenging task, even for humans. Current approaches to automatic sarcasm detection rely primarily on lexical and linguistic cues. This paper aims to address the di-cult task of sarcasm detection on Twitter by leveraging behavioral traits intrinsic to users expressing sarcasm. We identify such traits using the user's past tweets. We employ theories from behavioral and psychological studies to construct a behavioral modeling framework tuned for detecting sarcasm. We evaluate our framework and demonstrate its efficiency in identifying sarcastic tweets.

Original languageEnglish (US)
Title of host publicationWSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages97-106
Number of pages10
ISBN (Print)9781450333177
DOIs
Publication statusPublished - Feb 2 2015
Event8th ACM International Conference on Web Search and Data Mining, WSDM 2015 - Shanghai, China
Duration: Jan 31 2015Feb 6 2015

Other

Other8th ACM International Conference on Web Search and Data Mining, WSDM 2015
CountryChina
CityShanghai
Period1/31/152/6/15

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Keywords

  • Behavioral modeling
  • Sarcasm detection
  • Social media

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Rajadesingan, A., Zafarani, R., & Liu, H. (2015). Sarcasm detection on twitter: A behavioral modeling approach. In WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining (pp. 97-106). Association for Computing Machinery, Inc. https://doi.org/10.1145/2684822.2685316