research-article

DiHAN: A Novel Dynamic Hierarchical Graph Attention Network for Fake News Detection

Authors:

Jiaojiao Jiang,

Nan SunAuthors Info & Claims

CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

Pages 197 - 206

https://doi.org/10.1145/3627673.3679675

Published: 21 October 2024 Publication History

Abstract

The rapid spread of fake news on social media has caused great harm to society in recent years, which raises the detection of fake news as an urgent task. Recent methods utilize the interactions among different entities such as authors, subjects, and news articles to model news propagation as a static heterogeneous information network (HIN). However, this is suboptimal since fake news emerges dynamically, and the latent chronological interactions between news in HIN are essential signals for fake news detection. To this end, we model the dynamics of news and associated entities as a News-Driven Dynamic Heterogeneous Information Network (News-DyHIN), where the temporal relationships among news articles are well captured with meta-path based temporal neighbors. With the support of News-DyHIN, we propose a novel fake news detection framework, named <u> D </u>ynam<u> i </u>c <u> H </u>ierarchical <u> A </u>ttention <u> N </u>etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. In particular, DiHAN first employs a temporal node level attention to learn the temporal information from meta-path based news neighbors through the modeled News-DyHIN. Then, a semantic attention layer is adopted to fuse different types of meta-path based temporal information for news representation learning. Extensive evaluations conducted on two public real-world datasets demonstrate that our proposed DiHAN achieves significant improvements over established baseline models.

References

[1]

Hunt Allcott and Matthew Gentzkow. 2017. Social media and fake news in the 2016 election. Journal of economic perspectives, Vol. 31, 2 (2017), 211--36.

[2]

Marco T Bastos and Dan Mercea. 2019. The Brexit botnet and user-generated hyperpartisan news. Social science computer review, Vol. 37, 1 (2019), 38--54.

[3]

Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, and Junzhou Huang. 2020. Rumor detection on social media with bi-directional graph convolutional networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 549--556.

[4]

Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering, Vol. 30, 9 (2018), 1616--1637.

Digital Library

[5]

Shantanu Chandra, Pushkar Mishra, Helen Yannakoudakis, Madhav Nimishakavi, Marzieh Saeidi, and Ekaterina Shutova. 2020. Graph-based modeling of online communities for fake news detection. arXiv preprint arXiv:2008.06274 (2020).

[6]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

Digital Library

[7]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning, Vol. 20, 3 (1995), 273--297.

[8]

Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. 2019. Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9268--9277.

[9]

Yujie Fan, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye. 2022. Heterogeneous Temporal Graph Neural Network. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 657--665.

[10]

Guoyong Hu, Ye Ding, Shuhan Qi, Xuan Wang, and Qing Liao. 2019. Multi-depth graph convolutional networks for fake news detection. In CCF International conference on natural language processing and chinese computing. Springer, 698--710.

Digital Library

[11]

Haeseung Jeong. 2021. Hierarchical Attention Networks for Fake News Detection. Ph.,D. Dissertation. The Florida State University.

[12]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[13]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[14]

Qiuyan Li, Yanlei Shang, Xiuquan Qiao, and Wei Dai. 2020. Heterogeneous dynamic graph attention network. In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 404--411.

[15]

Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, and Min-Yen Kan. 2020. Fang: Leveraging social context for fake news detection using graph representation. In Proceedings of the 29th ACM international conference on information & knowledge management. 1165--1174.

Digital Library

[16]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701--710.

Digital Library

[17]

Yuxiang Ren and Jiawei Zhang. 2021. Fake news detection on news-oriented heterogeneous information networks through hierarchical graph attention. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.

[18]

Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020).

[19]

Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip. 2016. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering, Vol. 29, 1 (2016), 17--37.

Digital Library

[20]

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).

[21]

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

[22]

Kai Shu, Suhang Wang, and Huan Liu. 2017. Exploiting Tri-Relationship for Fake News Detection. arXiv preprint arXiv:1712.07709 (2017).

[23]

Chenguang Song, Kai Shu, and Bin Wu. 2021. Temporally evolving graph neural network for fake news detection. Information Processing & Management, Vol. 58, 6 (2021), 102712.

Digital Library

[24]

Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, Vol. 4, 11 (2011), 992--1003.

Digital Library

[25]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).

[26]

Petar Velivcković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).

[27]

Hanna M Wallach. 2006. Topic modeling: beyond bag-of-words. In Proceedings of the 23rd international conference on Machine learning. 977--984.

Digital Library

[28]

Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang. 2019. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. CoRR, Vol. abs/1909.01315 (2019). showeprint[arXiv]1909.01315 http://arxiv.org/abs/1909.01315

[29]

Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019. Heterogeneous graph attention network. In The world wide web conference. 2022--2032.

[30]

Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020).

[31]

Hansheng Xue, Luwei Yang, Wen Jiang, Yi Wei, Yi Hu, and Yu Lin. 2020. Modeling dynamic heterogeneous network for link prediction using hierarchical attention with temporal rnn. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 282--298.

[32]

Luwei Yang, Zhibo Xiao, Wen Jiang, Yi Wei, Yi Hu, and Hao Wang. 2020. Dynamic heterogeneous graph embedding using hierarchical attentions. In European Conference on Information Retrieval. Springer, 425--432.

Digital Library

Cited By

Li XLong JSong ZZhou T(2024)Fast Second-order Method for Neural Networks under Small Treewidth Setting2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825379(1029-1038)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825379

Index Terms

DiHAN: A Novel Dynamic Hierarchical Graph Attention Network for Fake News Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks

Recommendations

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. ...
MVAE: Multimodal Variational Autoencoder for Fake News Detection
WWW '19: The World Wide Web Conference

In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound ...
Fake News Early Detection: A Theory-driven Model
Field Notes

Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

October 2024

5705 pages

ISBN:9798400704369

DOI:10.1145/3627673

General Chairs:
Edoardo Serra
Boise State University, USA
,
Francesca Spezzano
Boise State University, USA

Copyright © 2024 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].

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 October 2024

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Australian Research Council

Conference

CIKM '24

Sponsor:

SIGIR

CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2024

ID, Boise, USA

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

1
Total Citations
View Citations
256
Total Downloads

Downloads (Last 12 months)256
Downloads (Last 6 weeks)34

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li XLong JSong ZZhou T(2024)Fast Second-order Method for Neural Networks under Small Treewidth Setting2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825379(1029-1038)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825379

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