Beyond news contents: The role of social context for fake news detection

Kai Shu, Suhang Wang, Huan Liu

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

1 Citation (Scopus)

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 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.

Original languageEnglish (US)
Title of host publicationWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages312-320
Number of pages9
ISBN (Electronic)9781450359405
DOIs
StatePublished - Jan 30 2019
Externally publishedYes
Event12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia
Duration: Feb 11 2019Feb 15 2019

Publication series

NameWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

Conference

Conference12th ACM International Conference on Web Search and Data Mining, WSDM 2019
CountryAustralia
CityMelbourne
Period2/11/192/15/19

Fingerprint

Ecosystems
Costs
Experiments

Keywords

  • Fake news detection
  • Joint learning
  • Social media mining

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

Cite this

Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. In WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 312-320). (WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3289600.3290994

Beyond news contents : The role of social context for fake news detection. / Shu, Kai; Wang, Suhang; Liu, Huan.

WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2019. p. 312-320 (WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining).

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

Shu, K, Wang, S & Liu, H 2019, Beyond news contents: The role of social context for fake news detection. in WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining. WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, pp. 312-320, 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, Australia, 2/11/19. https://doi.org/10.1145/3289600.3290994
Shu K, Wang S, Liu H. Beyond news contents: The role of social context for fake news detection. In WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc. 2019. p. 312-320. (WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining). https://doi.org/10.1145/3289600.3290994
Shu, Kai ; Wang, Suhang ; Liu, Huan. / Beyond news contents : The role of social context for fake news detection. WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2019. pp. 312-320 (WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining).
@inproceedings{ace6f0f6f33e4419826e279b5690b6dc,
title = "Beyond news contents: The role of social context for fake news detection",
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 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.",
keywords = "Fake news detection, Joint learning, Social media mining",
author = "Kai Shu and Suhang Wang and Huan Liu",
year = "2019",
month = "1",
day = "30",
doi = "10.1145/3289600.3290994",
language = "English (US)",
series = "WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc",
pages = "312--320",
booktitle = "WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining",

}

TY - GEN

T1 - Beyond news contents

T2 - The role of social context for fake news detection

AU - Shu, Kai

AU - Wang, Suhang

AU - Liu, Huan

PY - 2019/1/30

Y1 - 2019/1/30

N2 - 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 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.

AB - 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 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.

KW - Fake news detection

KW - Joint learning

KW - Social media mining

UR - http://www.scopus.com/inward/record.url?scp=85061758760&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061758760&partnerID=8YFLogxK

U2 - 10.1145/3289600.3290994

DO - 10.1145/3289600.3290994

M3 - Conference contribution

T3 - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

SP - 312

EP - 320

BT - WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

PB - Association for Computing Machinery, Inc

ER -