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Beyond News Contents: The Role of Social Context for Fake News Detection

Authors:

Huan LiuAuthors Info & Claims

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Pages 312 - 320

https://doi.org/10.1145/3289600.3290994

Published: 30 January 2019 Publication History

Abstract

Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users receive authentic information but also helps maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has the potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.

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Cited By

Alghamdi JLin YLuo S(2024)The Power of Context: A Novel Hybrid Context-Aware Fake News Detection ApproachInformation10.3390/info1503012215:3(122)Online publication date: 21-Feb-2024
https://doi.org/10.3390/info15030122
Padalko HChomko VChumachenko D(2024)A novel approach to fake news classification using LSTM-based deep learning modelsFrontiers in Big Data10.3389/fdata.2023.13208006Online publication date: 8-Jan-2024
https://doi.org/10.3389/fdata.2023.1320800
Martinez-Rico JAraujo LMartinez-Romo J(2024)Building a framework for fake news detection in the health domainPLOS ONE10.1371/journal.pone.030536219:7(e0305362)Online publication date: 8-Jul-2024
https://doi.org/10.1371/journal.pone.0305362
Show More Cited By

Index Terms

Beyond News Contents: The Role of Social Context for Fake News Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Wikis

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cover image ACM Conferences

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

January 2019

874 pages

ISBN:9781450359405

DOI:10.1145/3289600

General Chairs:
J. Shane Culpepper
RMIT University
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Paul N. Bennett
Microsoft
,
Kristina Lerman
University of Southern California

Copyright © 2019 ACM.

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Published: 30 January 2019

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WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining

February 11 - 15, 2019

Melbourne VIC, Australia

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WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;

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

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Cited By

Alghamdi JLin YLuo S(2024)The Power of Context: A Novel Hybrid Context-Aware Fake News Detection ApproachInformation10.3390/info1503012215:3(122)Online publication date: 21-Feb-2024
https://doi.org/10.3390/info15030122
Padalko HChomko VChumachenko D(2024)A novel approach to fake news classification using LSTM-based deep learning modelsFrontiers in Big Data10.3389/fdata.2023.13208006Online publication date: 8-Jan-2024
https://doi.org/10.3389/fdata.2023.1320800
Martinez-Rico JAraujo LMartinez-Romo J(2024)Building a framework for fake news detection in the health domainPLOS ONE10.1371/journal.pone.030536219:7(e0305362)Online publication date: 8-Jul-2024
https://doi.org/10.1371/journal.pone.0305362
Boukouvalas ZShafer A(2024)Role of Statistics in Detecting Misinformation: A Review of the State of the Art, Open Issues, and Future Research DirectionsAnnual Review of Statistics and Its Application10.1146/annurev-statistics-040622-03380611:1(27-50)Online publication date: 22-Apr-2024
https://doi.org/10.1146/annurev-statistics-040622-033806
Shusas E(2024)Designing Better Credibility Indicators: Understanding How Emerging Adults Assess Source Credibility of Misinformation Identification and LabelingCompanion Publication of the 2024 ACM Designing Interactive Systems Conference10.1145/3656156.3665126(41-44)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656156.3665126
Wang PJiao DYang LWang BYu R(2024)Hypergraph-based Truth Discovery for Sparse Data in Mobile CrowdsensingACM Transactions on Sensor Networks10.1145/364989420:3(1-23)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3649894
Pratelli MSaracco FPetrocchi M(2024)Unveiling News Publishers Trustworthiness Through Social InteractionsProceedings of the 16th ACM Web Science Conference10.1145/3614419.3644015(139-148)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3614419.3644015
Shusas EForte A(2024)Trust and Transparency: An Exploratory Study on Emerging Adults' Interpretations of Credibility Indicators on Social Media PlatformsExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650801(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650801
Zhang YMa XWu JYang JFan HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Heterogeneous Subgraph Transformer for Fake News DetectionProceedings of the ACM on Web Conference 202410.1145/3589334.3645680(1272-1282)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645680
Li HJiang YLi XWang CChen YLi H(2024)Perspective Collaboration for Multi-Domain Fake News DetectionInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142450003438:03Online publication date: 27-Mar-2024
https://doi.org/10.1142/S0218001424500034
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