zhenglong178 / fakenewsdetection

A repository for fake news detection.

fakenewsdetection

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.

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 or add pull request

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

• Survey
• Text-based Detection
• Multi-modal Detection
• Using Social Context
  • User Information
  • Comment
• Fact-checking
• Datasets

Survey

• An overview of online fake news: Characterization, detection, and discussion. [link]

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

• The Future of False Information Detection on Social Media: New Perspectives and Trends. [link]

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

• A survey of fake news: Fundamental theories, detection methods, and opportunities. [link]

  • 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]

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

• A Survey on Multimodal Disinformation Detection. [link]

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

• A Survey on Automated Fact-Checking [link].

  • 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] [code]

  • J Ma, W Gao, KF Wong. WWW, 2019.

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

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

• MDFEND: Multi-domain fake news detection. [link] [code]

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

• Convolutional neural network with margin loss for fake news detection. [link]

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

• Zoom Out and Observe: News Environment Perception for Fake News Detection. [link]

  • 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]

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

• Generalizing to the future: Mitigating entity bias in fake news detection. [link]

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

• Memory-guided multi-view multi-domain fake news detection. [link]

  • 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]

  • 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]

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

• MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning. [link]

  • 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]

  • 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]

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

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Multi-modal Detection

• EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. [link] [code]

  • 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] [code]

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

• Exploiting multi-domain visual information for fake news detection. [link]

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

• Spotfake+: A multimodal framework for fake news detection via transfer learning. [link]

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

• Multimodal disentangled domain adaption for social media event rumor detection. [link]

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

• Multimodal disentangled domain adaption for social media event rumor detection. [link]

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

• Multimodal fusion network with latent topic memory for rumor detection. [link]

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

• Hierarchical multi-modal contextual attention network for fake news detection. [link]

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

• Multimodal Fusion with Co-Attention Networks for Fake News Detection. [link]

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

• Multimodal emergent fake news detection via meta neural process networks. [link]

  • 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] [code]

  • 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]

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

• Detecting fake news by exploring the consistency of multimodal data. [link]

  • 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]

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

• Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection. [link]

  • 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]

  • 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].

  • 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].

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

• A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection. [link]

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

• Cross-modal ambiguity learning for multimodal fake news detection. [link]

  • 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]

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

• Cross-modal knowledge distillation in multi-modal fake news detection. [link]

  • 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]

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

• ARCNN framework for multimodal infodemic detection. [link]

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

• BCMF: A bidirectional cross-modal fusion model for fake news detection. [link]

  • 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]

  • 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]

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

• Improving Generalization for Multimodal Fake News Detection [link].

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

• MRML: Multimodal Rumor Detection by Deep Metric Learning [link].

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

• Graph Interactive Network with Adaptive Gradient for Multi-Modal Rumor Detection [link].

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

• Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion [link].

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

• Detecting and grounding multi-modal media manipulation. [link] [code]

  • R Shao, T Wu, Z Liu. CVPR, 2023.

• A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter. [link]

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

• Bootstrapping Multi-view Representations for Fake News Detection. [link] [code]

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

• Multimodal fake news analysis based on image–text similarity. [link]

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

• Multimodal fake news detection via progressive fusion networks. [link]

  • 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]

  • 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]

  • K Shu, S Wang, H Liu. WSDM, 2019.

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

  • YJ Lu, CT Li. ACL, 2020.

• FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. [link] [code]

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

• Hierarchical propagation networks for fake news detection: Investigation and exploitation. [link]

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

• FNED: a deep network for fake news early detection on social media. [link]

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

• Data Fusion Oriented Graph Convolution Network Model for Rumor Detection. [link]

  • 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]

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

• User Preference-aware Fake News Detection. [link] [code]

  • 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]

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

• Causal understanding of fake news dissemination on social media. [link]

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

• Temporally evolving graph neural network for fake news detection. [link]

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

• Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection. [link]

  • 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]

  • 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]

  • 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] [code]

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

• Towards Fine-Grained Reasoning for Fake News Detection. [link]

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

• Reinforcement Subgraph Reasoning for Fake News Detection. [link]

  • 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]

  • 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]

  • 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]

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

• A rumor & anti-rumor propagation model based on data enhancement and evolutionary game. [link]

  • 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] [code].

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

• Unsupervised Rumor Detection Based on Propagation Tree VAE [link].

  • 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]

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

Comment

• Weak Supervision for Fake News Detection via Reinforcement Learning. [link]

  • 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]

  • 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]

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

• EMET: Embeddings from multilingual-encoder transformer for fake news detection. [link]

  • S Schwarz, A Theóphilo. ICASSP, 2020.

• SeRN: Stance extraction and reasoning network for fake news detection. [link]

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

• Poligraph: Intrusion-tolerant and distributed fake news detection system. [link]

  • 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]

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

• gDART: Improving rumor verification in social media with Discrete Attention Representations. [link] [code]

  • 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]

  • 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]

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

• Cross-Modal Adversarial Contrastive Learning for Multi-Modal Rumor Detection [link].

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

• Human Cognition-based Consistency Inference Networks for Multi-modal Fake News Detection. [link]

  • 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]

  • N Vo, K Lee. SIGIR, 2018.

• Attributed multi-relational attention network for fact-checking url recommendation. [link]

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

• Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks [link].

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

• Learning from fact-checkers: Analysis and generation of fact-checking language. [link] [code]

  • N Vo, K Lee. SIGIR, 2019.

• DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning [link]

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

• Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks. [link]

  • 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] [code]

  • N Vo, K Lee. EMNLP, 2020.

• Evidence Inference Networks for Interpretable Claim Verification [link].

  • 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] [code]

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

• Kan: Knowledge-aware attention network for fake news detection. [link]

  • 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]

  • 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] [code]

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

• Fact-enhanced synthetic news generation. [link]

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

• Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection. [link]

  • 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]

  • 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]

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

• GERE: Generative Evidence Retrieval for Fact Verification [link]

  • 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]

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

• CrowdGraph: A Crowdsourcing Multi-modal Knowledge Graph Approach to Explainable Fauxtography Detection. [link]

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

• EvidenceNet: Evidence Fusion Network for Fact Verification. [link]

  • 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]

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

• MetaDetector: Meta Event Knowledge Transfer for Fake News Detection. [link]

  • 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]

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

• Fact-Checking Complex Claims with Program-Guided Reasoning. [link]

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

• Counterfactual Debiasing for Fact Verification. [link]

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

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

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