research-article

Rumor Gauge: Predicting the Veracity of Rumors on Twitter

Authors:

Soroush Vosoughi,

Mostafa ‘Neo’ Mohsenvand,

Deb RoyAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 4

Article No.: 50, Pages 1 - 36

https://doi.org/10.1145/3070644

Published: 14 July 2017 Publication History

Abstract

The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated verification of rumors (unverified information) that propagate through Twitter. To predict the veracity of rumors, we identified salient features of rumors by examining three aspects of information spread: linguistic style used to express rumors, characteristics of people involved in propagating information, and network propagation dynamics. The predicted veracity of a time series of these features extracted from a rumor (a collection of tweets) is generated using Hidden Markov Models. The verification algorithm was trained and tested on 209 rumors representing 938,806 tweets collected from real-world events, including the 2013 Boston Marathon bombings, the 2014 Ferguson unrest, and the 2014 Ebola epidemic, and many other rumors about various real-world events reported on popular websites that document public rumors. The algorithm was able to correctly predict the veracity of 75% of the rumors faster than any other public source, including journalists and law enforcement officials. The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.

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Cited By

Alsoubai APark JQadir SStringhini GRazi AWisniewski P(2024)Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media PlatformsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642315(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642315
Rashid JKim JMasood AChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Unraveling the Tangle of Disinformation: A Multimodal Approach for Fake News Identification on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651972(1849-1853)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651972
Tan XMao SXiao XYang Y(2024)Explainable rumor detection based on grey clustering: Fusion of manual features and deep learning featuresInformation Sciences10.1016/j.ins.2024.121055679(121055)Online publication date: Sep-2024
https://doi.org/10.1016/j.ins.2024.121055
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Index Terms

Rumor Gauge: Predicting the Veracity of Rumors on Twitter
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
      1. Information extraction
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 4

Special Issue on KDD 2016 and Regular Papers

November 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3119906

Editor:
Jie Tang
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2017 ACM.

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Publication History

Published: 14 July 2017

Accepted: 01 March 2017

Revised: 01 October 2016

Received: 01 November 2015

Published in TKDD Volume 11, Issue 4

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Cited By

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https://dl.acm.org/doi/10.1145/3613904.3642315
Rashid JKim JMasood AChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Unraveling the Tangle of Disinformation: A Multimodal Approach for Fake News Identification on Social MediaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651972(1849-1853)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651972
Tan XMao SXiao XYang Y(2024)Explainable rumor detection based on grey clustering: Fusion of manual features and deep learning featuresInformation Sciences10.1016/j.ins.2024.121055679(121055)Online publication date: Sep-2024
https://doi.org/10.1016/j.ins.2024.121055
Bajpai SChaturvedi A(2024)Toward Detecting Rumor Initiator in Online Social Networks Using Ontology-Driven ModelArabian Journal for Science and Engineering10.1007/s13369-024-08852-7Online publication date: 11-Mar-2024
https://doi.org/10.1007/s13369-024-08852-7
Sharma USingh J(2024)A comprehensive overview of fake news detection on social networksSocial Network Analysis and Mining10.1007/s13278-024-01280-314:1Online publication date: 24-Jun-2024
https://doi.org/10.1007/s13278-024-01280-3
Rani PShokeen J(2024)FNNet: a secure ensemble-based approach for fake news detection using blockchainThe Journal of Supercomputing10.1007/s11227-024-06216-4Online publication date: 29-May-2024
https://doi.org/10.1007/s11227-024-06216-4
Amutha R(2024)Detection on early dynamic rumor influence and propagation using biogeography-based optimization with deep learning approachesMultimedia Tools and Applications10.1007/s11042-024-18168-1Online publication date: 12-Mar-2024
https://doi.org/10.1007/s11042-024-18168-1
Luo NXie DMo YLi FTeng CJi D(2024)Joint rumour and stance identification based on semantic and structural information in social networksApplied Intelligence10.1007/s10489-023-05170-754:1(264-282)Online publication date: 1-Jan-2024
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Wu DTan ZZhao HJiang TQi M(2024)LIMFA: label-irrelevant multi-domain feature alignment-based fake news detection for unseen domainNeural Computing and Applications10.1007/s00521-023-09340-z36:10(5197-5215)Online publication date: 1-Apr-2024
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