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Probabilistic Approaches to Controversy Detection

Authors:

Shiri Dori-Hacohen,

James AllanAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2069 - 2072

https://doi.org/10.1145/2983323.2983911

Published: 24 October 2016 Publication History

Abstract

Recently, the problem of automated controversy detection has attracted a lot of interest in the information retrieval community. Existing approaches to this problem have set forth a number of detection algorithms, but there has been little effort to model the probability of controversy in a document directly. In this paper, we propose a probabilistic framework to detect controversy on the web, and investigate two models. We first recast a state-of-the-art controversy detection algorithm into a model in our framework. Based on insights from social science research, we also introduce a language modeling approach to this problem.

We evaluate different methods of creating controversy language models based on a diverse set of public datasets including Wikipedia, Web and News corpora. Our automatically derived language models show a significant relative improvement of 18% in AUC over prior work,and 23% over two manually curated lexicons.

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Cited By

Peiris PHerath TDissanayaka RSaranga KThelijjagoda SWeerathunge I(2023)Comprehensive Browser Extension for Analysing YouTube User Engagement, Controversy, User Requirements, and Trending Keywords2023 33rd International Telecommunication Networks and Applications Conference10.1109/ITNAC59571.2023.10368503(134-139)Online publication date: 29-Nov-2023
https://doi.org/10.1109/ITNAC59571.2023.10368503
de França Fdi Genova DPenteado CKamienski C(2023)Understanding conflict origin and dynamics on Twitter: A real-time detection systemExpert Systems with Applications10.1016/j.eswa.2022.118748212(118748)Online publication date: Feb-2023
https://doi.org/10.1016/j.eswa.2022.118748
Arroyo-Machado WTorres-Salinas DCostas R(2022)Wikinformetrics: Construction and description of an open Wikipedia knowledge graph data set for informetric purposesQuantitative Science Studies10.1162/qss_a_002263:4(931-952)Online publication date: 20-Dec-2022
https://doi.org/10.1162/qss_a_00226
Show More Cited By

Index Terms

Probabilistic Approaches to Controversy Detection
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Language models
    2. Retrieval tasks and goals
      1. Clustering and classification
      2. Sentiment analysis

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Publication History

Published: 24 October 2016

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National Science Foundation

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October 24 - 28, 2016

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Peiris PHerath TDissanayaka RSaranga KThelijjagoda SWeerathunge I(2023)Comprehensive Browser Extension for Analysing YouTube User Engagement, Controversy, User Requirements, and Trending Keywords2023 33rd International Telecommunication Networks and Applications Conference10.1109/ITNAC59571.2023.10368503(134-139)Online publication date: 29-Nov-2023
https://doi.org/10.1109/ITNAC59571.2023.10368503
de França Fdi Genova DPenteado CKamienski C(2023)Understanding conflict origin and dynamics on Twitter: A real-time detection systemExpert Systems with Applications10.1016/j.eswa.2022.118748212(118748)Online publication date: Feb-2023
https://doi.org/10.1016/j.eswa.2022.118748
Arroyo-Machado WTorres-Salinas DCostas R(2022)Wikinformetrics: Construction and description of an open Wikipedia knowledge graph data set for informetric purposesQuantitative Science Studies10.1162/qss_a_002263:4(931-952)Online publication date: 20-Dec-2022
https://doi.org/10.1162/qss_a_00226
Saquete ETomás DMoreda PMartínez-Barco PPalomar M(2022)Fighting post-truth using natural language processingExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.112943141:COnline publication date: 21-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2019.112943
Benslimane SAzé JBringay SServajean MMollevi C(2022)A text and GNN based controversy detection method on social mediaWorld Wide Web10.1007/s11280-022-01116-026:2(799-825)Online publication date: 17-Nov-2022
https://doi.org/10.1007/s11280-022-01116-0
Turenne N(2022)Net activism and whistleblowing on YouTube: a text mining analysisMultimedia Tools and Applications10.1007/s11042-022-13777-082:6(9201-9221)Online publication date: 29-Sep-2022
https://doi.org/10.1007/s11042-022-13777-0
Benslimane SAzé JBringay SServajean MMollevi C(2022)Controversy Detection: A Text and Graph Neural Network Based ApproachWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_26(339-354)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-90888-1_26
Morini VPollacci LRossetti G(2021)Toward a Standard Approach for Echo Chamber Detection: Reddit Case StudyApplied Sciences10.3390/app1112539011:12(5390)Online publication date: 10-Jun-2021
https://doi.org/10.3390/app11125390
Nugent TStelea NLeidner J(2021)Detecting Environmental, Social and Governance (ESG) Topics Using Domain-Specific Language Models and Data AugmentationFlexible Query Answering Systems10.1007/978-3-030-86967-0_12(157-169)Online publication date: 16-Sep-2021
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Küçük DCan F(2020)Stance DetectionACM Computing Surveys10.1145/336902653:1(1-37)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3369026
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