research-article

Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection

Authors:

Hao FanAuthors Info & Claims

SSDBM '23: Proceedings of the 35th International Conference on Scientific and Statistical Database Management

Article No.: 15, Pages 1 - 11

https://doi.org/10.1145/3603719.3603736

Published: 27 August 2023 Publication History

Abstract

The proliferation of fake news in social media has been recognized as a severe problem for society, and substantial attempts have been devoted to fake news detection to alleviate the detrimental impacts. Knowledge graphs (KGs) comprise rich factual relations among real entities, which could be utilized as ground-truth databases and enhance fake news detection. However, most of the existing methods only leveraged natural language processing and graph mining techniques to extract features of fake news for detection and rarely explored the ground knowledge in knowledge graphs. In this work, we propose a novel Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection (HGNNR4FD). The devised framework has four major components: 1) A heterogeneous graph (HG) built upon news content, including three types of nodes, i.e., news, entities, and topics, and their relations. 2) A KG that provides the factual basis for detecting fake news by generating embeddings via relations in the KG. 3) A novel attention-based heterogeneous graph neural network that can aggregate information from HG and KG, and 4) a fake news detector, which is capable of identifying fake news based on the news embeddings generated by HGNNR4FD. We further validate the performance of our method by comparison with seven state-of-art baselines and verify the effectiveness of the components through a thorough ablation analysis. From the results, we empirically demonstrate that our framework achieves superior results and yields improvement over the baselines regarding evaluation metrics of accuracy, precision, recall, and F1-score on four real-world datasets.

References

[1]

Sadia Afroz, Michael Brennan, and Rachel Greenstadt. 2012. Detecting hoaxes, frauds, and deception in writing style online. In S & P. IEEE, 461–475.

[2]

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993–1022.

Digital Library

[3]

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013).

[4]

Mu-Yen Chen, Yi-Wei Lai, and Jiunn-Woei Lian. 2022. Using Deep Learning Models to Detect Fake News About COVID-19. ACM Trans. Internet Technol. (2022).

[5]

Raveena Dayani, Nikita Chhabra, Taruna Kadian, and Rishabh Kaushal. 2015. Rumor detection in twitter: An analysis in retrospect. In ANTS, IEEE. 1–3.

[6]

Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018. Convolutional 2d knowledge graph embeddings. In AAAI, Vol. 32.

[7]

Zhiyu Ge. 2022. Knowledge Graphs and its Applications in Finance. In THE FUTURE AND FINTECH: ABCDI and Beyond. 155–214.

[8]

Mykhailo Granik and Volodymyr Mesyura. 2017. Fake news detection using naive Bayes classifier. In UKRCON. IEEE, 900–903.

[9]

Richard Hanna, Andrew Rohm, and Victoria L Crittenden. 2011. We’re all connected: The power of the social media ecosystem. Business horizons 54, 3 (2011), 265–273.

[10]

Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, and Ming Zhou. 2021. Compare to the knowledge: Graph neural fake news detection with external knowledge. In IJNLP (Volume 1: Long Papers). 754–763.

[11]

Qi Huang, Junshuai Yu, Jia Wu, and Bin Wang. 2020. Heterogeneous graph attention networks for early detection of rumors on twitter. In IJCNN. IEEE, 1–8.

[12]

Qi Huang, Chuan Zhou, Jia Wu, Luchen Liu, and Bin Wang. 2020. Deep spatial–temporal structure learning for rumor detection on Twitter. Neural Computing and Applications (2020), 1–11.

[13]

Qi Huang, Chuan Zhou, Jia Wu, Mingwen Wang, and Bin Wang. 2019. Deep structure learning for rumor detection on twitter. In IJCNN. IEEE, 1–8.

[14]

Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip Yu, and Weixiong Zhang. 2021. A survey of community detection approaches: From statistical modeling to deep learning. IEEE Transactions on Knowledge and Data Engineering (2021).

[15]

Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. 1746–1751.

[16]

Boshko Koloski, Timen Stepišnik Perdih, Marko Robnik-Šikonja, Senja Pollak, and Blaž Škrlj. 2022. Knowledge graph informed fake news classification via heterogeneous representation ensembles. Neurocomputing 496 (2022), 208–226.

Digital Library

[17]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML. 1188–1196.

[18]

Linfeng Li, Peng Wang, Jun Yan, Yao Wang, Simin Li, Jinpeng Jiang, Zhe Sun, Buzhou Tang, Tsung-Hui Chang, Shenghui Wang, 2020. Real-world data medical knowledge graph: construction and applications. Artificial intelligence in medicine 103 (2020), 101817.

[19]

Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, and Xiaoli Li. 2019. Heterogeneous graph attention networks for semi-supervised short text classification. In EMNLP-IJCNLP. 4821–4830.

[20]

Zhuang Liu, Wayne Lin, Ya Shi, and Jun Zhao. 2021. A robustly optimized BERT pre-training approach with post-training. In CCL 2021. Springer, 471–484.

Digital Library

[21]

Shikun Lyu and Dan Chia-Tien Lo. 2020. Fake news detection by decision tree. In 2020 SoutheastCon. IEEE, 1–2.

[22]

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. (2016).

[23]

Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z Sheng, Hui Xiong, and Leman Akoglu. 2021. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering (2021).

Digital Library

[24]

Xiaoxiao Ma, Jian Yang, Jia Wu, and Quan Z Sheng. 2023. Towards graph-level anomaly detection via deep evolutionary mapping. (2023).

[25]

Nikhil Mehta, María Leonor Pacheco, and Dan Goldwasser. 2022. Tackling fake news detection by continually improving social context representations using graph neural networks. In ACL.1363–1380.

[26]

Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Interspeech, Vol. 2. 1045–1048.

[27]

Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, and Min-Yen Kan. 2020. Fang: Leveraging social context for fake news detection using graph representation. In CIKM. 1165–1174.

[28]

Parth Patwa, Shivam Sharma, Srinivas Pykl, Vineeth Guptha, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das, and Tanmoy Chakraborty. 2021. Fighting an infodemic: Covid-19 fake news dataset. In ACL CONSTRAINT.21–29.

[29]

Juan Ramos 2003. Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning, Vol. 242. Citeseer, 29–48.

[30]

Francisco Rangel, Anastasia Giachanou, Bilal Hisham Hasan Ghanem, and Paolo Rosso. 2020. Overview of the 8th author profiling task at pan 2020: Profiling fake news spreaders on twitter. In CEUR-WS.org, Vol. 2696. 1–18.

[31]

Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In EMNLP. 2931–2937.

[32]

Victoria L Rubin, Niall Conroy, Yimin Chen, and Sarah Cornwell. 2016. Fake news or truth? using satirical cues to detect potentially misleading news. In CADD workshop proceedings. 7–17.

[33]

Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2020. Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big data 8, 3 (2020), 171–188.

[34]

Amit Singhal 2012. Introducing the knowledge graph: things, not strings. Official google blog 5 (2012), 16.

[35]

N Smitha and R Bharath. 2020. Performance comparison of machine learning classifiers for fake news detection. In ICIRCA. IEEE, 696–700.

[36]

Eugenio Tacchini, Gabriele Ballarin, Marco Luigi Della Vedova, Stefano Moret, Luca de Alfaro, 2017. Some like it Hoax: Automated fake news detection in social networks. In CEUR Workshop Proceedings. 1–15.

[37]

William Yang Wang. 2017. " liar, liar pants on fire": A new benchmark dataset for fake news detection. In ACL.422–426.

[38]

Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang. 2021. KEPLER: A unified model for knowledge embedding and pre-trained language representation. Transactions of the Association for Computational Linguistics 9 (2021), 176–194.

[39]

Jiyang Wu, Qiang Li, Qian Chen, Guangqiang Peng, Jinyu Wang, Qiang Fu, and Bo Yang. 2022. Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review. Energies 15, 21 (2022), 8031.

[40]

Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014).

[41]

Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, and Pietro Liò. 2023. State of the Art and Potentialities of Graph-level Learning. arxiv:2301.05860 [cs.LG]

[42]

Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Graph convolutional networks for text classification. In AAAI, Vol. 33. 7370–7377.

[43]

Ge Zhang, Jia Wu, Jian Yang, Amin Beheshti, Shan Xue, Chuan Zhou, and Quan Z Sheng. 2021. Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance. In ICDM. IEEE, 867–876.

[44]

Xinyi Zhou, Jindi Wu, and Reza Zafarani. 2020. SAFE: Similarity-Aware Multi-modal Fake News Detection. In PAKDD 2020, Vol. 12085. 354–367.

Cited By

Ma XLiu FWu JYang JXue SSheng Q(2025)Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and ObjectivesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350130737:2(881-895)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3501307
Zhang YMa XWu JYang JFan HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Heterogeneous Subgraph Transformer for Fake News DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645680(1272-1282)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645680
Xie BMa XWu JYang JFan H(2024)Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News DetectionIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332466170:1(2826-2837)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3324661
Show More Cited By

Index Terms

Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
      1. Social networks

Recommendations

Detection of Fake News Through Heterogeneous Graph Interactions
Networked Systems
Abstract
Fake news is one of the most prominent forms of disinformation. Unfortunately, today’s advanced social media platforms allow for the rapid transmission of fake news, which may negatively impact several aspects of human society. Despite the ...
Dynamic graph neural network for fake news detection
Highlights
- We study a novel problem of discrete-time dynamic news propagation network-based fake news detection task.
Abstract
The widespread of fake news on social media and other platforms can bring significant damage to the harmony and stability of our society. To defend against fake news, researchers have suggested various ways of dealing with fake news. ...
Fake news detection: A survey of graph neural network methods
Abstract
The emergence of various social networks has generated vast volumes of data. Efficient methods for capturing, distinguishing, and filtering real and fake news are becoming increasingly important, especially after the outbreak of the ...
Highlights
- All searchable articles of graph neural network (GNN) for fake news detection are reviewed.

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

SSDBM '23: Proceedings of the 35th International Conference on Scientific and Statistical Database Management

July 2023

232 pages

ISBN:9798400707469

DOI:10.1145/3603719

Copyright © 2023 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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Australian Research Council

Conference

SSDBM 2023

SSDBM 2023: 35th International Conference on Scientific and Statistical Database Management

July 10 - 12, 2023

CA, Los Angeles, USA

Acceptance Rates

Overall Acceptance Rate 56 of 146 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
279
Total Downloads

Downloads (Last 12 months)145
Downloads (Last 6 weeks)10

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ma XLiu FWu JYang JXue SSheng Q(2025)Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and ObjectivesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350130737:2(881-895)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3501307
Zhang YMa XWu JYang JFan HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Heterogeneous Subgraph Transformer for Fake News DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645680(1272-1282)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645680
Xie BMa XWu JYang JFan H(2024)Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News DetectionIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332466170:1(2826-2837)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3324661
Malik ABehera DHota JSwain A(2024)Ensemble Graph Neural Networks for Fake News Detection Using User Engagement and Text FeaturesResults in Engineering10.1016/j.rineng.2024.103081(103081)Online publication date: Oct-2024
https://doi.org/10.1016/j.rineng.2024.103081
Yan ZTang XLi ZYan X(2024)Social Media Oriented Fake News Detection Based on Social Context and Cascade GraphKnowledge and Systems Sciences10.1007/978-981-96-0178-3_15(213-224)Online publication date: 9-Nov-2024
https://doi.org/10.1007/978-981-96-0178-3_15

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.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten