An awesome paper list of fake news detection (FND) and rumor detection with 
Moreover, this is a personal list, if you have some additional literature, which need to be supplemented, you can feel free to drop an email (wangbing1416@gmail.com) to me!
- Fake News Detection
- Summarizations of FND
- Famous Chinese Researchers in FND
- ACL 2023, Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection [Paper]
- ACL 2023, Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [Paper]
- ACL 2022, Zoom Out and Observe: News Environment Perception for Fake News Detection [Paper]
- COLING 2022, A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection [Paper]
- COLING 2022, Demystifying Neural Fake News via Linguistic Feature-Based Interpretation [Paper]
- SIGIR 2022, Generalizing to the Future: Mitigating Entity Bias in Fake News Detection [Paper]
- ECML 2021, Early Detection of Fake News with Multi-source Weak Social Supervision [Paper]
- WWW 2021, Mining Dual Emotion for Fake News Detection [Paper]
- ICCART 2019, Fake News Detection via NLP is Vulnerable to Adversarial Attacks [Paper]
- COLING 2022, Improving Fake News Detection of Influential Domain via Domain-and Instance-Level Transfer [Paper]
- WWW 2022, Domain Adaptive Fake News Detection via Reinforcement Learning [Paper]
- TKDE 2022, Memory-Guided Multi-View Multi-Domain Fake News Detection [Paper]
- IPM 2022, Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo [Paper]
- ArXiv 2022, FuDFEND: Fuzzy-domain for Multi-domain Fake News Detection [Paper]
- CIKM 2021, MDFEND: Multi-domain Fake News Detection [Paper]
- ICONIP 2021, DAFD: Domain Adaptation Framework for Fake News Detection [Paper]
- IPM 2022, Fake News Detection via Knowledgeable Prompt Learning [Paper]
- AAAI 2021, KAN: Knowledge-aware Attention Network for Fake News Detection [Paper]
- ACL 2021, Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge [Paper]
- ACL 2022, Automatic Detection of Entity-Manipulated Text Using Factual Knowledge [Paper]
- COLING 2022, Threat Scenarios and Best Practices for Neural Fake News Detection [Paper]
- CL 2020, The Limitations of Stylometry for Detecting Machine-Generated Fake News [Paper]
- SIGIR 2022, Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention [Paper]
- WWW 2022, Evidence-aware Fake News Detection with Graph Neural Networks [Paper]
- ACL 2021, Automatic Fake News Detection: Are Models Learning to Reason? [Paper]
- CIKM 2021, Integrating Pattern-and Fact-based Fake News Detection via Model Preference Learning [Paper]
- ACL 2023, Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection [Paper]
- AAAI 2023, Bootstrapping Multi-view Representations for Fake News Detection [Paper]
- AAAI 2023, See How You Read? Multi-Reading Habits Fusion Reasoning for Multi-Modal Fake News Detection [Paper]
- AAAI 2023, FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms [Paper]
- AAAI 2023, COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning [Paper]
- TKDE 2023, Causal Inference for Leveraging Image-text Matching Bias in Multi-modal Fake News Detection [Paper]
- WWW 2022, Cross-modal Ambiguity Learning for Multimodal Fake News Detection [Paper]
- WWW 2022, A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection [Paper]
- ACL 2021, InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection [Paper]
- ACL 2021, Multimodal Fusion with Co-Attention Networks for Fake News Detection [Paper]
- ACL 2021, Edited Media Understanding Frames: Reasoning About the Intents and Implications of Visual Disinformation [Paper]
- CIKM 2021, Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic [Paper]
- KDD 2021, Multimodal Emergent Fake News Detection via Meta Neural Process Networks [Paper]
- IPM 2021, Detecting Fake News by Exploring the Consistency of Multimodal Data [Paper]
- MM 2021, Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues [Paper]
- SIGIR 2021, Hierarchical Multi-modal Contextual Attention Network for Fake News Detection [Paper]
- EMNLP 2020, Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [Paper]
- PAKDD 2020, SAFE: Similarity-Aware Multi-Modal Fake News Detection [Paper]
- WWW 2019, MVAE: Multimodal Variational Autoencoder for Fake News Detection [Paper]
- KDD 2018, EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection [Paper]
- AAAI 2023, HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection [Paper]
- KDD 2023, DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection [Paper]
- WWW 2023, Attacking Fake News Detectors via Manipulating News Social Engagement [Paper]
- AAAI 2022, Towards Fine-Grained Reasoning for Fake News Detection [Paper]
- ACL 2022, Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks [Paper]
- COLING 2022, Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection [Paper]
- COLING 2022, A Unified Propagation Forest-based Framework for Fake News Detection [Paper]
- COLING 2022, Topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks for Fake News Detection [Paper]
- KDD 2022, Reinforcement Subgraph Reasoning for Fake News Detection [Paper]
- WWW 2022, Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media [Paper]
- KDD 2021, Causal Understanding of Fake News Dissemination on Social Media [Paper]
- SIGIR 2021, User Preference-aware Fake News Detection [Paper]
- ACL 2023, DECKER: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification [Paper]
- ACL 2023, Counterfactual Debiasing for Fact Verification [Paper]
- ACL 2023, Fact-Checking Complex Claims with Program-Guided Reasoning [Paper]
- AAAI 2022, Synthetic Disinformation Attacks on Automated Fact Verification Systems [Paper]
- AAAI 2022, LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification [Paper]
- SIGIR 2022, GERE: Generative Evidence Retrieval for Fact Verification [Paper]
- WWW 2022, EvidenceNet: Evidence Fusion Network for Fact Verification [Paper]
- ACL 2021, Zero-shot Fact Verification by Claim Generation [Paper]
- ACL 2021, Unified Dual-view Cognitive Model for Interpretable Claim Verification [Paper]
- ACL 2021, Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification [Paper]
- ACL 2021, Structurizing Misinformation Stories via Rationalizing Fact-Checks [Paper]
- ACL 2021, Exploring Listwise Evidence Reasoning with T5 for Fact Verification [Paper]
- ACL 2021, Evidence-based Factual Error Correction [Paper]
- ACL Findings 2021, Strong and Light Baseline Models for Fact-Checking Joint Inference [Paper]
- ACL Findings 2021, A Multi-Level Attention Model for Evidence-Based Fact Checking [Paper]
- CIKM 2021, CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models [Paper]
- EMNLP 2021, Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification [Paper]
- NAACL 2021, Towards Few-Shot Fact-Checking via Perplexity [Paper]
- NAACL 2021, How Robust are Fact Checking Systems on Colloquial Claims? [Paper]
- NAACL 2022, The Role of Context in Detecting Previously Fact-Checked Claims [Paper]
- ACL 2021, Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims [Paper]
- ACL 2021, Claim Matching Beyond English to Scale Global Fact-Checking [Paper]
- ACL 2020, That is a Known Lie: Detecting Previously Fact-Checked Claims [Paper]
- EMNLP 2020, Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News [Paper]
- ACL 2022, FAVIQ: FAct Verification from Information-seeking Questions [Paper]
- ACL 2022, Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines [Paper]
- ACL 2021, COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic [Paper]
- KDD 2023, Rumor Detection with Diverse Counterfactual Evidence [Paper]
- AAAI 2023, Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning [Paper]
- AAAI 2022, DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media [Paper]
- COLING 2022, A Progressive Framework for Role-Aware Rumor Resolution [Paper]
- COLING 2022, Social Bot-Aware Graph Neural Network for Early Rumor Detection [Paper]
- NAACL 2022, Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning [Paper]
- WWW 2022, Rumor Detection on Social Media with Graph Adversarial Contrastive Learning [Paper]
- WWW 2022, Detecting False Rumors from Retweet Dynamics on Social Media [Paper]
- ACL Findings 2021, Adversary-Aware Rumor Detection [Paper]
- ACL Findings 2021, Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection [Paper]
- EMNLP 2021, Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks [Paper]
- EMNLP 2021, STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media [Paper]
- AAAI 2020, Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks [Paper]
- COLING 2020, Debunking Rumors on Twitter with Tree Transformer [Paper]
- ACL 2018, Rumor Detection on Twitter with Tree-structured Recursive Neural Networks [Paper]
- ACL 2021, Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection [Paper]
- AAAI 2020, Interpretable Rumor Detection in Microblogs by Attending to User Interactions [Paper]
- COLING 2022, Continually Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning [Paper]
- NAACL 2022, Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset [Paper]
- WWW 2021, Rumor Detection with Field of Linear and Non-Linear Propagation [Paper]
- EMNLP 2020, A State-independent and Time-evolving Network for Early Rumor Detection in Social Media [Paper]
- NAACL 2022, DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks [Paper]
- SIGIR ShortPaper 2021, Rumor Detection on Social Media with Event Augmentations [Paper]
- ICLR 2019, Data Augmentation for Rumor Detection using Context-sensitive Neural Language Model with Large-scale Credibility [Paper]
- IJCAI 2022, MFAN: Multi-modal Feature-enhanced Attention Networks for Rumor Detection [Paper]
- EMNLP 2021, Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection [Paper]
- MM 2019, Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection [Paper]
- MM 2017, Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs [Paper]
- SIGIR 2022, A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning [Paper]
- ACL ShortPaper 2019, Rumor Detection By Exploiting User Credibility Information, Attention and Multi-task Learning [Paper]
- COLING 2018, All-in-one: Multi-task Learning for Rumour Verification [Paper]
- WWW 2018, Detect Rumor and Stance Jointly by Neural Multi-task Learning [Paper]
1 Interpretability
2 Emergency -> low-resource setting, event-invariant features, temporal information
3 Select bias / social homophily -> edge augmentation
4 User profile -> user embeddings, historical posts
5 Unreliable connections -> graph reconstraction, edge reweighting
6 Temporal information
7 Robustness -> augmentation, adversarial learning
8 Stance detection
1 Interpretability
2 Emergency
3 Dynamicity (entity bias)
4 Domain adaptation -> pre-training, new datasets, adversarial learning, mixture of experts
5 Feature engineering -> emotion, writing style
6 Semi-supervised Learning
7 Robustness -> adversarial attack
8 Evidence-based FND
9 Multi-modal FND (ambiguity, alignment, emergency)
- Juan Cao, Institute of Computing Technology, Chinese Academy of Sciences
- Jing Ma, Department of Computer Science, Hong Kong Baptist University
- Huan Liu, Ira A. Fulton Schools of Engineering, Arizona State University
- Kai Shu, Department of Computer Science, Illinois Institute of Technology
- Songlin Hu, Institute of Information Engineering, Chinese Academy of Sciences
- Linmei Hu, Beijing Institute of Technology