research-article

Stance Classification through Proximity-based Community Detection

Authors:

Ophélie Fraisier,

Guillaume Cabanac,

Romaric Besançon,

Mohand BoughanemAuthors Info & Claims

HT '18: Proceedings of the 29th on Hypertext and Social Media

Pages 220 - 228

https://doi.org/10.1145/3209542.3209549

Published: 03 July 2018 Publication History

Abstract

Numerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection.

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Cited By

Kuo KWang MKao HDai YChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Advancing Stance Detection of Political Fan Pages: A Multimodal ApproachCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651467(702-705)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651467
Gambini MSenette CFagni TTesconi M(2024)Evaluating large language models for user stance detection on X (Twitter)Machine Language10.1007/s10994-024-06587-y113:10(7243-7266)Online publication date: 6-Sep-2024
https://dl.acm.org/doi/10.1007/s10994-024-06587-y
Budán PEscañuela Gonzalez MBudán MMartinez MSimari G(2023)Strength in coalitions: Community detection through argument similarityArgument & Computation10.3233/AAC-22000614:3(275-325)Online publication date: 7-Nov-2023
https://doi.org/10.3233/AAC-220006
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Index Terms

Stance Classification through Proximity-based Community Detection
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. World Wide Web
    1. Web mining

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Published In

cover image ACM Conferences

HT '18: Proceedings of the 29th on Hypertext and Social Media

July 2018

266 pages

ISBN:9781450354271

DOI:10.1145/3209542

General Chair:
Dongwon Lee
Penn State University, USA
,
Program Chairs:
Nishanth Sastry
King's College London, UK
,
Ingmar Weber
QCRI, Qatar

Copyright © 2018 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGCHI: Specialist Interest Group in Computer-Human Interaction of the ACM

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Association for Computing Machinery

New York, NY, United States

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Published: 03 July 2018

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HT '18: 29th ACM Conference on Hypertext and Social Media

July 9 - 12, 2018

MD, Baltimore, USA

Acceptance Rates

HT '18 Paper Acceptance Rate 19 of 69 submissions, 28%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Kuo KWang MKao HDai YChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Advancing Stance Detection of Political Fan Pages: A Multimodal ApproachCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651467(702-705)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651467
Gambini MSenette CFagni TTesconi M(2024)Evaluating large language models for user stance detection on X (Twitter)Machine Language10.1007/s10994-024-06587-y113:10(7243-7266)Online publication date: 6-Sep-2024
https://dl.acm.org/doi/10.1007/s10994-024-06587-y
Budán PEscañuela Gonzalez MBudán MMartinez MSimari G(2023)Strength in coalitions: Community detection through argument similarityArgument & Computation10.3233/AAC-22000614:3(275-325)Online publication date: 7-Nov-2023
https://doi.org/10.3233/AAC-220006
Abeysinghe BSunderraman R(2023)Inferring stances of silent-participants in Twitter chatter using Label Propagation2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW59300.2023.00138(824-831)Online publication date: May-2023
https://doi.org/10.1109/IPDPSW59300.2023.00138
Blanco GLourenço A(2023)A multilayered graph-based framework to explore behavioural phenomena in social media conversationsInternational Journal of Medical Informatics10.1016/j.ijmedinf.2023.105236179(105236)Online publication date: Nov-2023
https://doi.org/10.1016/j.ijmedinf.2023.105236
Alsaif HAldossari H(2023)Review of stance detection for rumor verification in social mediaEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105801119(105801)Online publication date: Mar-2023
https://doi.org/10.1016/j.engappai.2022.105801
Gambini MSenette CFagni TTesconi M(2023)From Tweets to Stance: An Unsupervised Framework for User Stance Detection on TwitterDiscovery Science10.1007/978-3-031-45275-8_7(96-110)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-45275-8_7
Rahman MLiu XSirrianni JAdams D(2022)Cross-issue correlation based opinion prediction in cyber argumentationArgument & Computation10.3233/AAC-20054413:2(209-247)Online publication date: 1-Jun-2022
https://doi.org/10.3233/AAC-200544
Elzanfaly DRadwan ZOthman N(2022)User Stance Detection and Prediction Considering Most Frequent InteractionsArtificial Intelligence and Online Engineering10.1007/978-3-031-17091-1_43(421-433)Online publication date: 15-Oct-2022
https://doi.org/10.1007/978-3-031-17091-1_43
Müller ARiedl JDrews W(2022)Real-Time Stance Detection and Issue Analysis of the 2021 German Federal Election Campaign on TwitterElectronic Government10.1007/978-3-031-15086-9_9(125-146)Online publication date: 30-Aug-2022
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