Fake News Detection

A repository for fake news detection.

fake news detection Learning Resources

This Github repository summarizes a list of fake news detection resources. For more details and the categorization criteria, please refer to our survey (opens new window).

We will try our best to continuously maintain this Github Repository in a weekly manner.

Contributing

Please help to contribute this list by contacting me (opens new window) or add pull request (opens new window)

Markdown format:

- Paper Name. 
  [[link]](link) 
  [[code]](link).
  - Author 1, Author 2, Author 3. *Conference/Journal*, Year.

Note: In the same year, please place the conference paper before the journal paper, as journals are usually submitted a long time ago and therefore have some lag. (i.e., Conferences-->Journals-->Preprints)

Table of Contents

• Fake News Detection
• fake news detection Learning Resources
  • Contributing
  • Table of Contents
  • Survey
  • Text-based Detection
  • Multi-modal Detection
  • Using Social Context
    • User Information
    • Comment
  • Fact-checking

Survey

• An overview of online fake news: Characterization, detection, and discussion. [link] (opens new window)

  • X Zhang, AA Ghorbani. Information Processing & Management, 2020.

• The Future of False Information Detection on Social Media: New Perspectives and Trends. [link] (opens new window)

  • B Guo, Y Ding, L Yao, Y Liang, Z Yu. ACM Computing Surveys, 2020.p

• A survey of fake news: Fundamental theories, detection methods, and opportunities. [link] (opens new window)

  • Mingfu Xue, Jian Wang, Weiqiang Liu. ACM Computing Surveys, 2021.

• A unified perspective for disinformation detection and truth discovery in social sensing: A survey. [link] (opens new window)

  • F Xu, VS Sheng, M Wang. ACM Computing Surveys, 2021.

• A Survey on Multimodal Disinformation Detection. [link] (opens new window)

  • F Alam, S Cresci, T Chakraborty, F Silvestri. COLING, 2022.

• A Survey on Automated Fact-Checking [link] (opens new window).

  • Z Guo, M Schlichtkrull, A Vlachos. Transactions of the Association for Computational Linguistics, 2022.

Text-based Detection

• Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning. [link] (opens new window) [code] (opens new window)

  • J Ma, W Gao, KF Wong. WWW, 2019.

• Conquering cross-source failure for news credibility: Learning generalizable representations beyond content embedding. [link] (opens new window)

  • YH Huang, TW Liu, SR Lee. WWW, 2020.

• MDFEND: Multi-domain fake news detection. [link] (opens new window) [code] (opens new window)

  • Q Nan, J Cao, Y Zhu, Y Wang, J Li. CIKM, 2021.

• Convolutional neural network with margin loss for fake news detection. [link] (opens new window)

  • MH Goldani, R Safabakhsh, S Momtazi. Information Processing & Management, 2021.

• Zoom Out and Observe: News Environment Perception for Fake News Detection. [link] (opens new window)

  • Q Sheng, J Cao, X Zhang, R Li, D Wang. ACL, 2022.

• Contrastive domain adaptation for early misinformation detection: A case study on covid-19. [link] (opens new window)

  • Z Yue, H Zeng, Z Kou, L Shang, D Wang. CIKM, 2022.

• Generalizing to the future: Mitigating entity bias in fake news detection. [link] (opens new window)

  • Y Zhu, Q Sheng, J Cao, S Li, D Wang. SIGIR, 2022.

• Memory-guided multi-view multi-domain fake news detection. [link] (opens new window)

  • Y Zhu, Q Sheng, J Cao, Q Nan, K Shu. IEEE Transactions on Knowledge and Data Engineering 2022.

• A network-based positive and unlabeled learning approach for fake news detection. [link] (opens new window)

  • MC de Souza, BM Nogueira, RG Rossi, RM Marcacini. Machine Learning, 2022.

• Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation. [link] (opens new window)

  • KH Huang, K McKeown, P Nakov, Y Choi, H Ji. ACL, 2023.

• MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning. [link] (opens new window)

  • Z Yue, H Zeng, Y Zhang, L Shang, D Wang. ACL, 2023.

• Improving rumor detection by promoting information campaigns with transformer-based generative adversarial learning. [link] (opens new window)

  • J Ma, J Li, W Gao, Y Yang. IEEE Transactions on Knowledge and Data Engineering, 2023.

• Meta-prompt based learning for low-resource false information detection. [link] (opens new window)

  • Y Huang, M Gao, J Wang, J Yin, K Shu, Q Fan. Information Processing & Management, 2023.

=======

Multi-modal Detection

• EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. [link] (opens new window) [code] (opens new window)

  • Y Wang, F Ma, Z Jin, Y Yuan, G Xun, K Jha, L Su, J Gao. KDD, 2018.

• MVAE: Multimodal Variational Autoencoder for Fake News Detection. [link] (opens new window) [code] (opens new window)

  • D Khattar, JS Goud, M Gupta, V Varma. WWW, 2019.

• Exploiting multi-domain visual information for fake news detection. [link] (opens new window)

  • P Qi, J Cao, T Yang, J Guo, J Li. ICDM, 2019.

• Spotfake+: A multimodal framework for fake news detection via transfer learning. [link] (opens new window)

  • S Singhal, A Kabra, M Sharma, RR Shah. AAAI, 2020.

• Multimodal disentangled domain adaption for social media event rumor detection. [link] (opens new window)

  • H Zhang, S Qian, Q Fang, C Xu. IEEE Transactions on Multimedia, 2020.

• Multimodal disentangled domain adaption for social media event rumor detection. [link] (opens new window)

  • H Zhang, S Qian, Q Fang, C Xu. IEEE Transactions on Multimedia, 2020.

• Multimodal fusion network with latent topic memory for rumor detection. [link] (opens new window)

  • J Chen, Z Wu, Z Yang, H Xie. ICME, 2021.

• Hierarchical multi-modal contextual attention network for fake news detection. [link] (opens new window)

  • S Qian, J Wang, J Hu, Q Fang, C Xu. SIGIR, 2021.

• Multimodal Fusion with Co-Attention Networks for Fake News Detection. [link] (opens new window)

  • Y Wu, P Zhan, Y Zhang, L Wang. ACL, 2021.

• Multimodal emergent fake news detection via meta neural process networks. [link] (opens new window)

  • Y Wang, F Ma, H Wang, K Jha, J Gao. KDD, 2021.

• Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic. [link] (opens new window) [code] (opens new window)

  • W Zhang, L Gui, Y He. CIKM, 2021.

• A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. [link] (opens new window)

  • C Song, N Ning, Y Zhang, B Wu. Information Processing & Management, 2021.

• Detecting fake news by exploring the consistency of multimodal data. [link] (opens new window)

  • J Xue, Y Wang, Y Tian, Y Li, L Shi, L Wei. Information Processing & Management, 2021.

• HAN, image captioning, and forensics ensemble multimodal fake news detection. [link] (opens new window)

  • P Meel, DK Vishwakarma. Information Sciences, 2021.

• Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection. [link] (opens new window)

  • P Li, X Sun, H Yu, Y Tian, F Yao. IEEE Transactions on Multimedia, 2021.

• Multi-modal meta multi-task learning for social media rumor detection. [link] (opens new window)

  • H Zhang, S Qian, Q Fang, C Xu. IEEE Transactions on Multimedia, 2021.

• Multi-Modal Adversarial Adaptive Network for Misinformation Detection on Social Media [link] (opens new window).

  • L Zhang, P Zhang, X Zhu, L Liu, H Xu. ICME, 2022.

• AdaDebunk: An Efficient and Reliable Deep State Space Model for Adaptive Fake News Early Detection [link] (opens new window).

  • K Li, B Guo, S Ren, Z Yu. CIKM, 2022.

• A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection. [link] (opens new window)

  • L Shang, Z Kou, Y Zhang, D Wang. WWW, 2022.

• Cross-modal ambiguity learning for multimodal fake news detection. [link] (opens new window)

  • Y Chen, D Li, P Zhang, J Sui, Q Lv, L Tun. WWW, 2022.

• Leveraging Intra and Inter Modality Relationship for Multimodal Fake News Detection. [link] (opens new window)

  • S Singhal, T Pandey, S Mrig, RR Shah. WWW, 2022.

• Cross-modal knowledge distillation in multi-modal fake news detection. [link] (opens new window)

  • Z Wei, H Pan, L Qiao, X Niu, P Dong. ICASSP, 2022.

• Cross-Platform Multimodal Misinformation: Taxonomy, Characteristics and Detection for Textual Posts and Videos. [link] (opens new window)

  • N Micallef, M Sandoval-Castañeda, A Cohen. ICWSM, 2022.

• ARCNN framework for multimodal infodemic detection. [link] (opens new window)

  • C Raj, P Meel. Neural Networks, 2022.

• BCMF: A bidirectional cross-modal fusion model for fake news detection. [link] (opens new window)

  • C Yu, Y Ma, L An, G Li. Information Processing & Management, 2022.

• Causal Inference for Leveraging Image-text Matching Bias in Multi-modal Fake News Detection. [link] (opens new window)

  • L Hu, Z Chen, ZZJ Yin, L Nie. IEEE Transactions on Knowledge and Data Engineering, 2022.

• Understanding the Use and Abuse of Social Media: Generalized Fake News Detection With a Multichannel Deep Neural Network. [link] (opens new window)

  • RK Kaliyar, A Goswami, P Narang. IEEE Transactions on Computational Social Systems, 2022.

• Improving Generalization for Multimodal Fake News Detection [link] (opens new window).

  • S Tahmasebi, S Hakimov, R Ewerth. ICMR, 2023.

• MRML: Multimodal Rumor Detection by Deep Metric Learning [link] (opens new window).

  • L Peng, S Jian, D Li, S Shen. ICASSP, 2023.

• Graph Interactive Network with Adaptive Gradient for Multi-Modal Rumor Detection [link] (opens new window).

  • T Sun, Z Qian, P Li, Q Zhu. ICMR, 2023.

• Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion [link] (opens new window).

  • Y Zhou, Y Yang, Q Ying, Z Qian, X Zhang. ICMR, 2023.

• Detecting and grounding multi-modal media manipulation. [link] (opens new window) [code] (opens new window)

  • R Shao, T Wu, Z Liu. CVPR, 2023.

• A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter. [link] (opens new window)

  • U Naseem, J Kim, M Khushi, AG Dunn. WSDM, 2023.

• Bootstrapping Multi-view Representations for Fake News Detection. [link] (opens new window) [code] (opens new window)

  • Q Ying, X Hu, Y Zhou, Z Qian, D Zeng. AAAI, 2023.

• Multimodal fake news analysis based on image–text similarity. [link] (opens new window)

  • X Zhang, S Dadkhah, AG Weismann. IEEE Transactions on Computational Social Systems, 2023.

• Multimodal fake news detection via progressive fusion networks. [link] (opens new window)

  • J Jing, H Wu, J Sun, X Fang, H Zhang. Information Processing & Management, 2023.

• Positive Unlabeled Fake News Detection Via Multi-Modal Masked Transformer Network. [link] (opens new window)

  • J Wang, S Qian, J Hu, R Hong. IEEE Transactions on Multimedia, 2023.

Using Social Context

User Information

• Beyond News Contents: The Role of Social Context for Fake News Detection. [link] (opens new window)

  • K Shu, S Wang, H Liu. WSDM, 2019.

• GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. [link] (opens new window) [code] (opens new window)

  • YJ Lu, CT Li. ACL, 2020.

• FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. [link] (opens new window) [code] (opens new window)

  • VH Nguyen, K Sugiyama, P Nakov. *CIKM *, 2020.

• Hierarchical propagation networks for fake news detection: Investigation and exploitation. [link] (opens new window)

  • K Shu, D Mahudeswaran, S Wang, H Liu. ICWSM, 2020.

• FNED: a deep network for fake news early detection on social media. [link] (opens new window)

  • Y Liu, YFB Wu. ACM Transactions on Information Systems, 2020.

• Data Fusion Oriented Graph Convolution Network Model for Rumor Detection. [link] (opens new window)

  • K Yu, H Jiang, T Li, S Han, X Wu. IEEE Transactions on Network and Service Management, 2020.

• Discovering differential features: Adversarial learning for information credibility evaluation. [link] (opens new window)

  • L Wu, Y Rao, A Nazir, H Jin. Information Sciences, 2020.

• User Preference-aware Fake News Detection. [link] (opens new window) [code] (opens new window)

  • Y Dou, K Shu, C Xia, PS Yu, L Sun. SIGIR, 2021.

• Rumor detection on social media with graph structured adversarial learning. [link]

  • X Yang, Y Lyu, T Tian, Y Liu, Y Liu, X Zhang. IJCAI, 2021.

• Embracing domain differences in fake news: Cross-domain fake news detection using multi-modal data. [link] (opens new window)

  • A Silva, L Luo, S Karunasekera, C Leckie. AAAI, 2021.

• Causal understanding of fake news dissemination on social media. [link] (opens new window)

  • L Cheng, R Guo, K Shu, H Liu. KDD, 2021.

• Temporally evolving graph neural network for fake news detection. [link] (opens new window)

  • C Song, K Shu, B Wu. Information Processing & Management, 2021.

• Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection. [link] (opens new window)

  • A Silva, Y Han, L Luo, S Karunasekera. Information Processing & Management, 2021.

• Studying and understanding characteristics of post-syncing practice and goal in social network sites. [link] (opens new window)

  • P Zhang, B Liu, X Ding, T Lu, H Gu, N Gu. ACM Transactions on the Web, 2021.

• Mistr: A multiview structural-temporal learning framework for rumor detection. [link] (opens new window)

  • J Li, P Bao, H Shen, X Li. IEEE Transactions on Big Data, 2021.

• Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media. [link] (opens new window) [code] (opens new window)

  • E Min, Y Rong, Y Bian, T Xu, P Zhao, J Huang. WWW, 2022.

• Towards Fine-Grained Reasoning for Fake News Detection. [link] (opens new window)

  • Y Jin, X Wang, R Yang, Y Sun, W Wang. AAAI, 2022.

• Reinforcement Subgraph Reasoning for Fake News Detection. [link] (opens new window)

  • R Yang, X Wang, Y Jin, C Li, J Lian, X Xie. KDD, 2022.

• Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information. [link] (opens new window)

  • J Cui, K Kim, SH Na, S Shin. CIKM, 2022.

• MFAN: Multi-modal Feature-enhanced Attention Networks for Rumor Detection. [link]

  • J Zheng, X Zhang, S Guo, Q Wang, W Zang, Y Zhang. IJCAI, 2022.

• An integrated multi-task model for fake news detection. [link] (opens new window)

  • Q Liao, H Chai, H Han, X Zhang. IEEE Transactions on Knowledge and Data Engineering, 2022.

• A hierarchical network-oriented analysis of user participation in misinformation spread on WhatsApp. [link] (opens new window)

  • GP Nobre, CHG Ferreira, JM Almeida. Information Processing & Management, 2022.

• A rumor & anti-rumor propagation model based on data enhancement and evolutionary game. [link] (opens new window)

  • Y Xiao, W Li, S Qiang, Q Li, H Xiao. IEEE Transactions on Emerging Topics in Computing, 2022.

• Learning Sparse Alignments via Optimal Transport for Cross-Domain Fake News Detection [link] (opens new window) [code] (opens new window).

  • W Tang, Z Ma, H Sun, J Wang. ICASSP, 2023.

• Unsupervised Rumor Detection Based on Propagation Tree VAE [link] (opens new window).

  • L Fang, K Feng, K Zhao, A Hu, T, Li. IEEE Transactions on Knowledge and Data Engineering, 2023.

• Preventing profiling for ethical fake news detection. [link] (opens new window)

  • L Allein, MF Moens, D Perrotta. Information Processing & Management, 2023.

Comment

• Weak Supervision for Fake News Detection via Reinforcement Learning. [link] (opens new window)

  • Y Wang, W Yang, F Ma, J Xu, B Zhong. AAAI, 2020.

• QSAN: A quantum-probability based signed attention network for explainable false information detection. [link] (opens new window)

  • T Tian, Y Liu, X Yang, Y Lyu, X Zhang. CIKM, 2020.

• Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection. [link] (opens new window)

  • L Zhong, J Cao, Q Sheng, J Guo, Z Wang. ACL, 2020.

• EMET: Embeddings from multilingual-encoder transformer for fake news detection. [link] (opens new window)

  • S Schwarz, A Theóphilo. ICASSP, 2020.

• SeRN: Stance extraction and reasoning network for fake news detection. [link] (opens new window)

  • J Xie, S Liu, R Liu, Y Zhang. ICASSP, 2021.

• Poligraph: Intrusion-tolerant and distributed fake news detection system. [link] (opens new window)

  • G Shan, B Zhao, JR Clavin, H Zhang. IEEE Transactions on Information Forensics and Security, 2021.

• What and Why Towards Duo Explainable Fauxtography Detection under Constrained Supervision. [link] (opens new window)

  • Z Kou, D Zhang, L Shang. IEEE Transactions on Big Data, 2021.

• gDART: Improving rumor verification in social media with Discrete Attention Representations. [link] (opens new window) [code] (opens new window)

  • S Roy, M Bhanu, S Saxena, S Dandapat. Information Processing & Management, 2022.

• Dynamic probabilistic graphical model for progressive fake news detection on social media platform. [link] (opens new window)

  • K Li, B Guo, J Liu, J Wang, H Ren, F Yi. ACM Transactions on Intelligent Systems and Technology, 2022.

• Explainable Detection of Fake News on Social Media Using Pyramidal Co-Attention Network. [link] (opens new window)

  • F Khan, R Alturki, G Srivastava. IEEE Transactions on Computational Social Systems, 2022.

• Cross-Modal Adversarial Contrastive Learning for Multi-Modal Rumor Detection [link] (opens new window).

  • T Zou, Z Qian, P Li, Q Zhu. ICASSP, 2023.

• Human Cognition-based Consistency Inference Networks for Multi-modal Fake News Detection. [link] (opens new window)

  • L Wu, P Liu, Y Zhao, P Wang. IEEE Transactions on Knowledge and Data Engineering, 2023.

Fact-checking

• The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News [link] (opens new window)

  • N Vo, K Lee. SIGIR, 2018.

• Attributed multi-relational attention network for fact-checking url recommendation. [link] (opens new window)

  • D You, N Vo, K Lee, Q Liu. CIKM, 2019.

• Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks [link] (opens new window).

  • J Ma, W Gao, S Joty, KF Wong. ACL, 2019.

• Learning from fact-checkers: Analysis and generation of fact-checking language. [link] (opens new window) [code] (opens new window)

  • N Vo, K Lee. SIGIR, 2019.

• DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning [link] (opens new window)

  • K Popat, S Mukherjee, A Yates, G Weikum. EMNLP, 2020.

• Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks. [link] (opens new window)

  • Y Wang, S Qian, J Hu, Q Fang, C Xu. ICMR, 2020.

• Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News. [link] (opens new window) [code] (opens new window)

  • N Vo, K Lee. EMNLP, 2020.

• Evidence Inference Networks for Interpretable Claim Verification [link] (opens new window).

  • L Wu, Y Rao, L Sun, W He. AAAI, 2021.

• Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification

  • L Wu, Y Rao, X Yang, W Wang, A Nazir. IJCAI, 2021.

• Mining Dual Emotion for Fake News Detection. [link] (opens new window) [code] (opens new window)

  • X Zhang, J Cao, X Li, Q Sheng, L Zhong. WWW, 2021.

• Kan: Knowledge-aware attention network for fake news detection. [link] (opens new window)

  • Y Dun, K Tu, C Chen, C Hou, X Yuan. AAAI, 2021.

• Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues. [link] (opens new window)

  • P Qi, J Cao, X Li, H Liu, Q Sheng, X Mi, Q He. ACM MM, 2021.

• Integrating pattern-and fact-based fake news detection via model preference learning. [link] (opens new window) [code] (opens new window)

  • Q Sheng, X Zhang, J Cao, L Zhong. CIKM, 2021.

• Fact-enhanced synthetic news generation. [link] (opens new window)

  • K Shu, Y Li, K Ding, H Liu. AAAI, 2021.

• Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection. [link] (opens new window)

  • S Qian, J Hu, Q Fang, C Xu. ACM Transactions on Multimedia Computing, Communications, and Applications, 2021.

• Evidence-aware Fake News Detection with Graph Neural Networks [link] (opens new window)

  • W Xu, J Wu, Q Liu, S Wu, L Wang. WWW, 2022.

• Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources [link] (opens new window)

  • S Abdelnabi, R Hasan, M Fritz. CVPR, 2022.

• GERE: Generative Evidence Retrieval for Fact Verification [link] (opens new window)

  • J Chen, R Zhang, J Guo, Y Fan, X Cheng. SIGIR, 2022.

• Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention

  • J Wu, Q Liu, W Xu, S Wu. SIGIR, 2022.

• “This is Fake! Shared it by Mistake”: Assessing the Intent of Fake News Spreaders. [link] (opens new window)

  • X Zhou, K Shu, VV Phoha, H Liu. WWW, 2022.

• CrowdGraph: A Crowdsourcing Multi-modal Knowledge Graph Approach to Explainable Fauxtography Detection. [link] (opens new window)

  • Z Kou, Y Zhang, D Zhang, D Wang. HCI, 2022.

• EvidenceNet: Evidence Fusion Network for Fact Verification. [link] (opens new window)

  • Z Chen, SC Hui, F Zhuang, L Liao, F Li, M Jia. WWW, 2022.

• The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers. [link] (opens new window)

  • A Giachanou, B Ghanem, EA Ríssola, P Rosso. Data & Knowledge Engineering, 2022.

• MetaDetector: Meta Event Knowledge Transfer for Fake News Detection. [link] (opens new window)

  • Y Ding, B Guo, Y Liu, Y Liang, H Shen. ACM Transactions on Intelligent Systems and Technology, 2022.

• FactKG: Fact Verification via Reasoning on Knowledge Graphs. [link] (opens new window)

  • J Kim, S Park, Y Kwon, Y Jo, J Thorne, E Choi. ACL, 2023.

• Fact-Checking Complex Claims with Program-Guided Reasoning. [link] (opens new window)

  • L Pan, X Wu, X Lu, A Luu, W Wang, M Kan, P Nakov. ACL, 2023.

• Counterfactual Debiasing for Fact Verification. [link] (opens new window)

  • W Xu, Q Liu, S Wu, L Wang. ACL, 2023.

• Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection. [link] (opens new window)

  • M Sun, X Zhang, J Ma, S Xie, Y Liu. IEEE Transactions on Knowledge and Data Engineering, 2023.