research-article

FANG: Leveraging Social Context for Fake News Detection Using Graph Representation

Authors:

Van-Hoang Nguyen,

Kazunari Sugiyama,

Min-Yen KanAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 1165 - 1174

https://doi.org/10.1145/3340531.3412046

Published: 19 October 2020 Publication History

Abstract

We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph. Our experimental results show that FANG is better at capturing the social context into a high fidelity representation, compared to recent graphical and non-graphical models. In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data. We further demonstrate that the representations learned by FANG generalize to related tasks, such as predicting the factuality of reporting of a news medium.

Supplementary Material

MP4 File (3340531.3412046.mp4)

This is an updated version.

Download
43.74 MB

References

[1]

O. Ajao, D. Bhowmik, and S. Zargari. 2019. Sentiment Aware Fake News Detection on Online Social Networks. In ICASSP. 2507--2511.

[2]

Mihael Ankerst, Markus M Breunig, Hans-Peter Kriegel, and Jörg Sander. 1999. OPTICS: Ordering Points to Identify the Clustering Structure. ACM SIGMOD Record, Vol. 28, 2 (1999), 49--60.

Digital Library

[3]

Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predicting Factuality of Reporting and Bias of News Media Sources. In EMNLP. 3528--3539.

[4]

Levi Boxell, Matthew Gentzkow, and Jesse M. Shapiro. 2017. Is the Internet Causing Political Polarization? Evidence from Demographics. Technical Report NBER Working Paper No. 23258. National Bureau of Economic Research.

[5]

Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, Vol. 34, 4 (2017), 18--42.

[6]

Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information Credibility on Twitter. In WWW. 675--684.

[7]

Kareem Darwish, Dimitar Alexandrov, Preslav Nakov, and Yelena Mejova. 2017. Seminar Users in the Arabic Twitter Sphere. In SocInfo. 91--108.

[8]

Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017. SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours. In SemEval-2017. 69--76.

[9]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.

[10]

Ming Dong, Bolong Zheng, Nguyen Quoc Viet Hung, Han Su, and Guohui Li. 2019. Multiple Rumor Source Detection with Graph Convolutional Networks. In CIKM. 569--578.

[11]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In KDD. 855--864.

[12]

William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034.

[13]

Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, Minumol Joseph, Aaditya Kulkarni, Anil K. Nayak, Vikas Sable, Chengkai Li, and Mark Tremayne. 2017. ClaimBuster: The First-ever End-to-end Fact-checking System. VLDB, Vol. 10, 12 (2017), 1945--1948.

Digital Library

[14]

Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, and Kazunari Sugiyama. 2014. Predicting the Popularity of Web 2.0 Items based on User Comments. In SIGIR. 233--242.

[15]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation, Vol. 9, 8 (1997), 1735--1780.

Digital Library

[16]

Zhiwei Jin, Juan Cao, Yu-Gang Jiang, and Yongdong Zhang. 2014. News Credibility Evaluation on Microblog with a Hierarchical Propagation Model. In ICDM. 230--239.

[17]

Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. 2016. News Verification by Exploiting Conflicting Social Viewpoints in Microblogs. In AAAI. 2972--2978.

[18]

Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. MVAE: Multimodal Variational Autoencoder for Fake News Detection. In WWW. 2915--2921.

[19]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.

[20]

Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018. PHEME dataset for Rumour Detection and Veracity Classification. (6 2018). https://figshare.com/articles/dataset/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078

[21]

Vivek Kulkarni, Junting Ye, Steven Skiena, and William Yang Wang. 2018. Multi-view Models for Political Ideology Detection of News Articles. In EMNLP. 3518--3527.

[22]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692 (2019).

[23]

Yang Liu and Yi-Fang Brook Wu. 2018. Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks. In AAAI. 354--361.

[24]

Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective Approaches to Attention-based Neural Machine Translation. In EMNLP. 1412--1421.

[25]

Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting Rumors from Microblogs with Recurrent Neural Networks. In IJCAI. 3818--3824.

[26]

Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. 2015. Detect Rumors Using Time Series of Social Context Information on Microblogging Websites. In CIKM. 1751--1754.

[27]

Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. SemEval-2016 Task 6: Detecting Stance in Tweets. In SemEval-2016. 31--41.

[28]

Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, and Alessandro Moschitti. 2018. Automatic Stance Detection Using End-to-End Memory Networks. In NAACL-HLT. 767--776.

[29]

Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein. 2019. Fake News Detection on Social Media using Geometric Deep Learning. arXiv preprint arXiv:1902.06673 (2019).

[30]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In EMNLP. 1532--1543.

[31]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In KDD. 701--710.

[32]

Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2016. Credibility Assessment of Textual Claims on the Web. In CIKM. 2173--2178.

[33]

Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017. Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media. In WWW. 1003--1012.

[34]

Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2018. CredEye: A Credibility Lens for Analyzing and Explaining Misinformation. In WWW. 155--158.

[35]

Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. CSI: A Hybrid Deep Model for Fake News Detection. In CIKM. 797--806.

[36]

Gerald Salton and Michael J. McGill. 1983. Introduction to Modern Information Retrieval. McGraw-Hill.

Digital Library

[37]

Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2018. FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. arXiv preprint arXiv:1809.01286 (2018).

[38]

Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 19, 1 (2017), 22--36.

Digital Library

[39]

Kai Shu, Suhang Wang, and Huan Liu. 2019. Beyond News Contents: The Role of Social Context for Fake News Detection. In WSDM. 312--320.

[40]

Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017. A Dataset for Multi-Target Stance Detection. In EACL. 551--557.

[41]

Zoe Thomas. 2020. WHO says fake coronavirus claims causing `infodemic'. BBC News (13th Feb, 2020). https://www.bbc.com/news/technology-51497800

[42]

James Thorne and Andreas Vlachos. 2018. Automated Fact Checking: Task Formulations, Methods and Future Directions. In COLING. 3346--3359.

[43]

Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. In KDD. 849--857.

Digital Library

[44]

Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. 2012. Automatic Detection of Rumor on Sina Weibo. In MDS. 1--7.

[45]

Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, and Songlin Hu. 2019. Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection. In ICDM. 796--805.

Cited By

Plikynas DRizgelienė IKorvel G(2025)Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine LearningIEEE Access10.1109/ACCESS.2025.353068813(17583-17629)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530688
Nan QSheng QCao JZhu YWang DYang GLi J(2025)Exploiting user comments for early detection of fake news prior to users’ commentingFrontiers of Computer Science10.1007/s11704-024-40674-619:10Online publication date: 28-Jan-2025
https://doi.org/10.1007/s11704-024-40674-6
Gao YZhu QShi XJin H(2025)A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic GraphsBig Data10.1007/978-981-96-1024-2_15(202-217)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_15
Show More Cited By

Index Terms

FANG: Leveraging Social Context for Fake News Detection Using Graph Representation

Recommendations

Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web
WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or “trolls.” ...
Spread of misinformation on social media: What contributes to it and how to combat it
Abstract
With the prevalence and increasing impact of misinformation on social media, numerous studies have explored the characteristics of misinformation. However, we lack a holistic understanding of what factors contribute to the viral spread of ...
Highlights
- This systematic review summarizes findings from 423 studies that were published between 2010 and 2021
- Factors affecting the spread of misinformation are categorized into four aspects: source, message, context, and receiver
- ...
On sentiment of online fake news
ASONAM '20: Proceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

The presence of disinformation and fake news on the Internet and especially social media has become a major concern. Prime examples of such fake news surged in the 2016 U.S. presidential election cycle and the COVID-19 pandemic. We quantify sentiment ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Best Paper

Author Tags

Qualifiers

Research-article

Conference

CIKM '20

Sponsor:

CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

Acceptance Rates

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

160
Total Citations
View Citations
2,927
Total Downloads

Downloads (Last 12 months)328
Downloads (Last 6 weeks)18

Reflects downloads up to 31 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Plikynas DRizgelienė IKorvel G(2025)Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine LearningIEEE Access10.1109/ACCESS.2025.353068813(17583-17629)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530688
Nan QSheng QCao JZhu YWang DYang GLi J(2025)Exploiting user comments for early detection of fake news prior to users’ commentingFrontiers of Computer Science10.1007/s11704-024-40674-619:10Online publication date: 28-Jan-2025
https://doi.org/10.1007/s11704-024-40674-6
Gao YZhu QShi XJin H(2025)A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic GraphsBig Data10.1007/978-981-96-1024-2_15(202-217)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_15
Siciliano FMaiano LPapa LBaccini FAmerini ISilvestri F(2025)Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News Without Modifying itMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_44(525-530)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74627-7_44
Li YChu ZJia CZu B(2024)SAMGAT: structure-aware multilevel graph attention networks for automatic rumor detectionPeerJ Computer Science10.7717/peerj-cs.220010(e2200)Online publication date: 18-Jul-2024
https://doi.org/10.7717/peerj-cs.2200
Catyanadika PSabani ALeenders M(2024)Exploring Data Veracity Management in a Post-Truth Business EnvironmentJournal of Database Management10.4018/JDM.36172635:1(1-46)Online publication date: 30-Nov-2024
https://doi.org/10.4018/JDM.361726
Zhu YLi YWang JGao MWei J(2024)FaKnow: A Unified Library for Fake News DetectionData Intelligence10.3724/2096-7004.di.2024.0026Online publication date: 26-Aug-2024
https://doi.org/10.3724/2096-7004.di.2024.0026
Harris SHadi HAhmad NAlshara M(2024)Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research AgendasTechnologies10.3390/technologies1211022212:11(222)Online publication date: 6-Nov-2024
https://doi.org/10.3390/technologies12110222
Zhao WHe PZeng ZXu X(2024)Fake News Detection Based on Knowledge-Guided Semantic AnalysisElectronics10.3390/electronics1302025913:2(259)Online publication date: 5-Jan-2024
https://doi.org/10.3390/electronics13020259
Lin CWang YLi CFan WXu JWang Q(2024)A Cross-Lingual Media Profiling Model for Detecting Factuality and Political BiasApplied Sciences10.3390/app1421983714:21(9837)Online publication date: 28-Oct-2024
https://doi.org/10.3390/app14219837
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten