research-article

Sarcasm Detection on Twitter: A Behavioral Modeling Approach

Authors:

Ashwin Rajadesingan,

Huan LiuAuthors Info & Claims

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

Pages 97 - 106

https://doi.org/10.1145/2684822.2685316

Published: 02 February 2015 Publication History

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 difficult 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.

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Cited By

Ou LLi Z(2025)Modeling inter-modal incongruous sentiment expressions for multi-modal sarcasm detectionNeurocomputing10.1016/j.neucom.2024.128874616(128874)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128874
Khan SQasim IKhan WAurangzeb KKhan JAnwar M(2024)A novel transformer attention‐based approach for sarcasm detectionExpert Systems10.1111/exsy.13686Online publication date: 23-Jul-2024
https://doi.org/10.1111/exsy.13686
Prashanth KRamakrishnudu T(2024)Sarcasm‐based tweet‐level stress detectionExpert Systems10.1111/exsy.1353441:4Online publication date: 10-Jan-2024
https://doi.org/10.1111/exsy.13534
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Index Terms

Sarcasm Detection on Twitter: A Behavioral Modeling Approach
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

February 2015

482 pages

ISBN:9781450333177

DOI:10.1145/2684822

General Chairs:
Xueqi Cheng
ICT, Chinese Academy of Sciences, China
,
Hang Li
Huawei Technologies, China
,
Program Chairs:
Evgeniy Gabrilovich
Google, USA
,
Jie Tang
Tsinghua University, China

Copyright © 2015 ACM.

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Published: 02 February 2015

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WSDM 2015: Eighth ACM International Conference on Web Search and Data Mining

February 2 - 6, 2015

Shanghai, China

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WSDM '15 Paper Acceptance Rate 39 of 238 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Ou LLi Z(2025)Modeling inter-modal incongruous sentiment expressions for multi-modal sarcasm detectionNeurocomputing10.1016/j.neucom.2024.128874616(128874)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128874
Khan SQasim IKhan WAurangzeb KKhan JAnwar M(2024)A novel transformer attention‐based approach for sarcasm detectionExpert Systems10.1111/exsy.13686Online publication date: 23-Jul-2024
https://doi.org/10.1111/exsy.13686
Prashanth KRamakrishnudu T(2024)Sarcasm‐based tweet‐level stress detectionExpert Systems10.1111/exsy.1353441:4Online publication date: 10-Jan-2024
https://doi.org/10.1111/exsy.13534
Nirmala MGandomi ABabu MBabu LPatan R(2024)An Emoticon-Based Novel Sarcasm Pattern Detection Strategy to Identify Sarcasm in Microblogging Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.330690811:4(5319-5326)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3306908
Liu YZhang YSong D(2024)A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327914515:1(326-341)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3279145
Mahmud TAkter TAziz MKamal Uddin MHossain MAndersson K(2024)Integration of NLP and Deep Learning for Automated Fake News Detection2024 Second International Conference on Inventive Computing and Informatics (ICICI)10.1109/ICICI62254.2024.00072(398-404)Online publication date: 11-Jun-2024
https://doi.org/10.1109/ICICI62254.2024.00072
M J ARavikumar P(2024)Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study2024 Second International Conference on Advances in Information Technology (ICAIT)10.1109/ICAIT61638.2024.10690577(1-10)Online publication date: 24-Jul-2024
https://doi.org/10.1109/ICAIT61638.2024.10690577
Puvvada SKusuma Shaik MGalla KVenkatrama Phani Kumar SVenkata Krishna Kishore K(2024)A Novel Machine Learning Model for Sarcasm Detection on Facebook Comments2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC60222.2024.10575012(546-552)Online publication date: 5-Jun-2024
https://doi.org/10.1109/ICAAIC60222.2024.10575012
Diao YYang LLi SHao ZFan XLin H(2024)Detect Sarcasm and Humor Jointly by Neural Multi-Task LearningIEEE Access10.1109/ACCESS.2024.337085812(38071-38080)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3370858
Hussain KSaeed ZAbbasi RSindhu MKhattak AArafat SDaud AMushtaq M(2024)Towards Understanding the Role of Content-based and Contextualized Features in Detecting Abuse on TwitterHeliyon10.1016/j.heliyon.2024.e29593(e29593)Online publication date: Apr-2024
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