User Preference-aware Fake News Detection

Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored. The confirmation bias theory has indicated that a user is more likely to spread a piece of fake news when it confirms his/her existing beliefs/preferences. Users' historical, social engagements such as posts provide rich information about users' preferences toward news and have great potentials to advance fake news detection. However, the work on exploring user preference for fake news detection is somewhat limited. Therefore, in this paper, we study the novel problem of exploiting user preference for fake news detection. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. We release our code and data as a benchmark for GNN-based fake news detection: https://github.com/safe-graph/GNN-FakeNews.

[1]  Nadeem Ahmad,et al.  Personality Assessment using Twitter Tweets , 2017, KES.

[2]  Davide Eynard,et al.  Fake News Detection on Social Media using Geometric Deep Learning , 2019, ArXiv.

[3]  William Yang Wang.  “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection , 2017, ACL.

[4]  Huan Liu,et al.  Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation , 2019, ICWSM.

[5]  Yi Han,et al.  Graph Neural Networks with Continual Learning for Fake News Detection from Social Media , 2020, ArXiv.

[6]  Chengkai Li,et al.  ClaimBuster: The First-ever End-to-end Fact-checking System , 2017, Proc. VLDB Endow..

[7]  Philip S. Yu,et al.  Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT , 2020, ArXiv.

[8]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[9]  Helen Yannakoudakis,et al.  Graph-based Modeling of Online Communities for Fake News Detection , 2020, ArXiv.

[10]  Sungyong Seo,et al.  CSI: A Hybrid Deep Model for Fake News Detection , 2017, CIKM.

[11]  Fenglong Ma,et al.  EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection , 2018, KDD.

[12]  Mor Naaman,et al.  VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter , 2021, ICWSM.

[13]  H. Russell Bernard,et al.  Studying Fake News via Network Analysis: Detection and Mitigation , 2018, Lecture Notes in Social Networks.

[14]  Paolo Rosso,et al.  Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter , 2020, CLEF.

[15]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[16]  Cheng-Te Li,et al.  GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media , 2020, ACL.

[17]  R. Zafarani,et al.  A Survey of Fake News , 2020, ACM Comput. Surv..

[18]  Pushpak Bhattacharyya,et al.  Your Sentiment Precedes You: Using an author’s historical tweets to predict sarcasm , 2015, WASSA@EMNLP.

[19]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[20]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[21]  Jure Leskovec,et al.  How Powerful are Graph Neural Networks? , 2018, ICLR.

[22]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[23]  Huan Liu,et al.  FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media , 2018, Big Data.

[24]  Philip S. Yu,et al.  Mixup-Transfomer: Dynamic Data Augmentation for NLP Tasks , 2020, ArXiv.

[25]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[26]  Mai ElSherief,et al.  Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection , 2018, NAACL.

[27]  Jure Leskovec,et al.  Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.

[28]  Reza Zafarani,et al.  SAFE: Similarity-Aware Multi-Modal Fake News Detection , 2020, PAKDD.

[29]  L. Ross,et al.  Naive realism in everyday life: Implications for social conflict and misunderstanding. , 1996 .

[30]  R. Nickerson.  Confirmation Bias: A Ubiquitous Phenomenon in Many Guises , 1998 .

[31]  Ye Zhang,et al.  A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification , 2015, IJCNLP.