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https://github.com/wangbing1416/Awesome-Fake-News-Detection

An awesome paper list of fake news detection (FND) and rumor detection.
https://github.com/wangbing1416/Awesome-Fake-News-Detection

List: Awesome-Fake-News-Detection

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An awesome paper list of fake news detection (FND) and rumor detection.

Awesome Lists containing this project

README 

        



## Fake News Detection



An awesome paper list of **fake news detection (FND)** and **rumor detection** with ![](https://img.shields.io/badge/162-red) papers. FND methods are divided into context-based and social media-based methods.  

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 ([email protected]) to me!



- [Fake News Detection](#fake-news-detection)

  - [Context-based FND](#context-based-fnd)

    - [Text-only Methods](#text-only-methods)

      - [Supervised Learning](#supervised-learning)

      - [Domain Adaptation](#domain-adaptation)

      - [Knowledge base-based](#knowledge-base-based)

      - [Machine-generated News Detection](#machine-generated-news-detection)

    - [Evidence-aware Methods](#evidence-aware-methods)

    - [Multi-modal Methods](#large-language-model-based-methods)

    - [Large Language Model-based Methods](#multi-modal-methods)

  - [Social Media-based FND](#social-media-based-fnd)

- [Fact-check & Fact Verification](#fact-check--fact-verification)

  - [Supervised Methods](#supervised-methods)

  - [LLM Based Methods](#llm-based-methods)

  - [Multi-hop Fact Verification](#multi-hop-fact-verification)

  - [Previously Fact-check](#previously-fact-check)

  - [New Datasets](#new-datasets)

- [Rumor Detection](#rumor-detection)

  - [Supervised Methods](#supervised-methods-1)

    - [with Unreliable Propagations](#with-unreliable-propagations)

    - [with Temporal Features](#with-temporal-features)

    - [with User Profile](#with-user-profile)

    - [with Data Augmentation](#with-data-augmentation)

  - [Multi-modal Methods](#multi-modal-methods-1)

  - [Joint Stance & Rumor Detection](#joint-stance--rumor-detection)

- [Summarizations of FND](#summarizations-of-fnd)

    - [Social Media-based Fake News Detection and Rumor Detection](#social-media-based-fake-news-detection-and-rumor-detection)

    - [Context-based Fake News Detection](#context-based-fake-news-detection)

- [Famous Chinese Researchers in FND](#famous-chinese-researchers-in-fnd)



## Context-based FND



### Text-only Methods



#### Supervised Learning

**[CIKM 2024]** Why Misinformation is Created? Detecting them by Integrating Intent Features [[Paper](https://dl.acm.org/doi/pdf/10.1145/3627673.3679799)]  

**[CIKM 2024]** Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media [[Paper](https://dl.acm.org/doi/pdf/10.1145/3627673.3679919)]  

**[WWW 2024]** MSynFD: Multi-hop Syntax Aware Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3589334.3645468)]  

**[ACL 2023]** Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection [[Paper](https://arxiv.org/pdf/2306.14728)]  

**[ACL 2023]** Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [[Paper](https://arxiv.org/pdf/2203.05386)]  

**[ACL 2022]** Zoom Out and Observe: News Environment Perception for Fake News Detection [[Paper](https://arxiv.org/pdf/2203.10885.pdf)]  

**[COLING 2022]** A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection [[Paper](https://arxiv.org/pdf/2209.14642.pdf)]  

**[COLING 2022]** Demystifying Neural Fake News via Linguistic Feature-Based Interpretation [[Paper](https://aclanthology.org/2022.coling-1.573.pdf)]  

**[SIGIR 2022]** Generalizing to the Future: Mitigating Entity Bias in Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3477495.3531816)]  

**[ECML 2021]** Early Detection of Fake News with Multi-source Weak Social Supervision [[Paper](http://www.cs.iit.edu/~kshu/files/ecml_pkdd_mwss.pdf)]  

**[WWW 2021]** Mining Dual Emotion for Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3442381.3450004)]  

**[ICCART 2019]** Fake News Detection via NLP is Vulnerable to Adversarial Attacks [[Paper](https://arxiv.org/ftp/arxiv/papers/1901/1901.09657.pdf)]  



#### Domain Adaptation



**[COLING 2022]** Improving Fake News Detection of Influential Domain via Domain-and Instance-Level Transfer [[Paper](https://arxiv.org/pdf/2209.08902.pdf)]  

**[WWW 2022]** Domain Adaptive Fake News Detection via Reinforcement Learning [[Paper](https://arxiv.org/pdf/2202.08159.pdf)]  

**[TKDE 2022]** Memory-Guided Multi-View Multi-Domain Fake News Detection [[Paper](https://ieeexplore.ieee.org/document/9802916)]  

**[IPM 2022]** Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo [[Paper](https://www.sciencedirect.com/science/article/pii/S0306457322000784?via%3Dihub)]  

**[ArXiv 2022]** FuDFEND: Fuzzy-domain for Multi-domain Fake News Detection [[Paper](https://arxiv.org/ftp/arxiv/papers/2205/2205.03778.pdf)]  

**[CIKM 2021]** MDFEND: Multi-domain Fake News Detection [[Paper](https://arxiv.org/pdf/2201.00987.pdf)]  

**[ICONIP 2021]** DAFD: Domain Adaptation Framework for Fake News Detection [[Paper](http://www.cs.iit.edu/~kshu/files/DAFD_ICONIP.pdf)]  



#### Knowledge base-based



**[IPM 2022]** Fake News Detection via Knowledgeable Prompt Learning [[Paper](https://www.sciencedirect.com/science/article/pii/S030645732200139X)]  

**[AAAI 2021]** KAN: Knowledge-aware Attention Network for Fake News Detection [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/16080)]  

**[ACL 2021]** Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge [[Paper](https://aclanthology.org/2021.acl-long.62.pdf)]  



#### Machine-generated News Detection

**[ICLR 2024]** Can LLM-Generated Misinformation Be Detected? [[Paper](https://openreview.net/pdf?id=ccxD4mtkTU)]  

**[ACL 2022]** Automatic Detection of Entity-Manipulated Text Using Factual Knowledge [[Paper](https://aclanthology.org/2022.acl-short.10.pdf)]  

**[COLING 2022]** Threat Scenarios and Best Practices for Neural Fake News Detection [[Paper](https://aclanthology.org/2022.coling-1.106.pdf)]  

**[CL 2020]** The Limitations of Stylometry for Detecting Machine-Generated Fake News [[Paper](https://aclanthology.org/2020.cl-2.8.pdf)]  



### Evidence-aware Methods



**[KDD 2023]** MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3580305.3599873)]  

**[SIGIR 2022]** Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention [[Paper](https://web.archive.org/web/20220712063219id_/https://dl.acm.org/doi/pdf/10.1145/3477495.3531850)]  

**[WWW 2022]** Evidence-aware Fake News Detection with Graph Neural Networks [[Paper](https://dl.acm.org/doi/pdf/10.1145/3485447.3512122)]  

**[ACL 2021]** Automatic Fake News Detection: Are Models Learning to Reason? [[Paper](https://arxiv.org/pdf/2105.07698.pdf)]  

**[CIKM 2021]** Integrating Pattern-and Fact-based Fake News Detection via Model Preference Learning [[Paper](https://arxiv.org/pdf/2109.11333.pdf)]  



### Multi-modal Methods

**[MM 2024]** FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs [[Paper](https://dl.acm.org/doi/pdf/10.1145/3664647.3681089)]  

**[MM 2024]** Mitigating Social Hazards: Early Detection of Fake News via Diffusion-Guided Propagation Path Generation [[Paper](https://dl.acm.org/doi/pdf/10.1145/3664647.3681087)]  

**[MM 2024]** Harmfully Manipulated Images Matter in Multimodal Misinformation Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3664647.3681322)]  

**[MM 2024]** MMDFND: Multi-modal Multi-Domain Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3664647.3681317)]  

**[CIKM 2024]** Multimodal Misinformation Detection using Large Vision-Language Models [[Paper](https://dl.acm.org/doi/pdf/10.1145/3627673.3679826)]  

**[SIGIR 2024]** Fake News Detection via Multi-scale Semantic Alignment and Cross-modal Attention [[Paper](https://dl.acm.org/doi/pdf/10.1145/3626772.3657905)]  

**[WWW 2024]** MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3589334.3645385)]  

**[ACL 2024]** Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection [[Paper](https://aclanthology.org/2024.acl-long.316.pdf)]  

**[AAAI 2024]** Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/28677/29315)]  

**[CVPR 2024]** SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection [[Paper](https://arxiv.org/pdf/2403.03170.pdf)]  

**[Arxiv 2024]** FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs [[Paper](https://arxiv.org/pdf/2403.01988.pdf)]  

**[Arxiv 2024]** LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation [[Paper](https://arxiv.org/pdf/2402.11943.pdf)]  

**[ACL 2023]** Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors [[Paper](https://arxiv.org/pdf/2306.05241)]  

**[AAAI 2023]** FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms [[Paper](https://ojs.aaai.org/index.php/AAAI/article/download/26689/26461)]  

**[MM 2023]** Cross-modal Contrastive Learning for Multimodal Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3581783.3613850)]  

**[MM 2023]** Combating Online Misinformation Videos: Characterization, Detection, and Future Directions [[Paper](https://dl.acm.org/doi/pdf/10.1145/3581783.3612426)]  

**[ACL 2023]** Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection [[Paper](https://aclanthology.org/2023.acl-long.37.pdf)]  

**[AAAI 2023]** Bootstrapping Multi-view Representations for Fake News Detection [[Paper](https://openreview.net/pdf?id=tS2AkSDLYZ)]  

**[AAAI 2023]** See How You Read? Multi-Reading Habits Fusion Reasoning for Multi-Modal Fake News Detection [[Paper](https://ojs.aaai.org/index.php/AAAI/article/download/26609/26381)]  

**[AAAI 2023]** FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms [[Paper](https://ojs.aaai.org/index.php/AAAI/article/download/26689/26461)]  

**[AAAI 2023]** COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning [[Paper](https://arxiv.org/pdf/2101.06278)]  

**[TKDE 2023]** Causal Inference for Leveraging Image-text Matching Bias in Multi-modal Fake News Detection [[Paper](https://ieeexplore.ieee.org/abstract/document/9996587/)]  

**[WWW 2022]** Cross-modal Ambiguity Learning for Multimodal Fake News Detection [[Paper](https://web.archive.org/web/20220428130656id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3511968)]  

**[WWW 2022]** A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection [[Paper](https://web.archive.org/web/20220503034453id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512257)]  

**[ACL 2021]** InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection [[Paper](https://aclanthology.org/2021.acl-long.133.pdf)]  

**[ACL 2021]** Multimodal Fusion with Co-Attention Networks for Fake News Detection [[Paper](https://aclanthology.org/2021.findings-acl.226.pdf)]  

**[ACL 2021]** Edited Media Understanding Frames: Reasoning About the Intents and Implications of Visual Disinformation [[Paper](https://aclanthology.org/2021.acl-long.158.pdf)]  

**[CIKM 2021]** Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection [[Paper](https://dl.acm.org/doi/abs/10.1145/3459637.3482212)]  

**[CIKM 2021]** Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic [[Paper](https://arxiv.org/ftp/arxiv/papers/2109/2109.01850.pdf)]  

**[KDD 2021]** Multimodal Emergent Fake News Detection via Meta Neural Process Networks [[Paper](https://dl.acm.org/doi/pdf/10.1145/3447548.3467153)]  

**[IPM 2021]** Detecting Fake News by Exploring the Consistency of Multimodal Data [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0306457321001060)]  

**[MM 2021]** Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues [[Paper](https://arxiv.org/pdf/2108.10509.pdf)]  

**[SIGIR 2021]** Hierarchical Multi-modal Contextual Attention Network for Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3462871)]  

**[EMNLP 2020]** Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [[Paper](https://arxiv.org/pdf/2009.07698.pdf)]  

**[PAKDD 2020]** SAFE: Similarity-Aware Multi-Modal Fake News Detection [[Paper](https://link.springer.com/chapter/10.1007/978-3-030-47436-2_27)]  

**[WWW 2019]** MVAE: Multimodal Variational Autoencoder for Fake News Detection [[Paper](https://dl.acm.org/doi/abs/10.1145/3308558.3313552)]  

**[KDD 2018]** EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3219819.3219903)]  



### Large Language Model-based Methods

**[ACL Findings 2024]** DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection [[Paper](https://aclanthology.org/2024.findings-acl.155.pdf)]  

**[WWW 2024]** Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom [[Paper](https://dl.acm.org/doi/pdf/10.1145/3589334.3645471)]  

**[Arxiv 2024]** Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors [[Paper](https://arxiv.org/pdf/2403.09747)]  

**[NAACL 2024]** Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation [[Paper](https://arxiv.org/pdf/2403.14952)]  

**[CIKM 2024]** Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models [[Paper](https://dl.acm.org/doi/pdf/10.1145/3627673.3679519)]  

**[AAAI 2024]** Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection [[Paper](https://arxiv.org/pdf/2309.12247)]  



---



## Social Media-based FND



**[AAAI 2023]** HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/25655/25427)]  

**[KDD 2023]** DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3580305.3599298)]  

**[WWW 2023]** Attacking Fake News Detectors via Manipulating News Social Engagement [[Paper](https://arxiv.org/pdf/2302.07363)]  

**[AAAI 2022]** Towards Fine-Grained Reasoning for Fake News Detection [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/20517)]  

**[ACL 2022]** Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks [[Paper](https://aclanthology.org/2022.acl-long.97.pdf)]  

**[COLING 2022]** Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection [[Paper](https://aclanthology.org/2022.coling-1.243.pdf)]  

**[COLING 2022]** A Unified Propagation Forest-based Framework for Fake News Detection [[Paper](https://aclanthology.org/2022.coling-1.244.pdf)]  

**[COLING 2022]** Topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks for Fake News Detection [[Paper](https://aclanthology.org/2022.coling-1.415.pdf)]  

**[KDD 2022]** Reinforcement Subgraph Reasoning for Fake News Detection [[Paper](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/KDD2022_FakeNewsDetection_camera_ready.pdf)]  

**[WWW 2022]** Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media [[Paper](https://www.atailab.cn/seminar2022fall/pdf/2022_WWW_Divide-and-Conquer_Post-User%20Interaction%20Network%20for%20Fake%20News%20Detection%20on%20Social%20Media.pdf)]  

**[KDD 2021]** Causal Understanding of Fake News Dissemination on Social Media [[Paper](https://arxiv.org/pdf/2010.10580.pdf)]  

**[SIGIR 2021]** User Preference-aware Fake News Detection [[Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3462990)]  



---



## Fact-check & Fact Verification



### Supervised Methods

**[NAACL 2024]** Fact Checking Beyond Training Set [[Paper](https://arxiv.org/pdf/2403.18671)]  

**[ICLR 2024]** Unsupervised Pretraining for Fact Verification by Language Model Distillation [[Paper](https://openreview.net/pdf?id=1mjsP8RYAw)]  

**[SIGIR 2023]** Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence [[Paper](https://dl.acm.org/doi/pdf/10.1145/3539618.3592049)]  

**[ACL 2023]** DECKER: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification [[Paper](https://aclanthology.org/2023.findings-acl.752)]  

**[ACL 2023]** Counterfactual Debiasing for Fact Verification [[Paper](https://aclanthology.org/2023.acl-long.374.pdf)]  

**[ACL 2023]** Fact-Checking Complex Claims with Program-Guided Reasoning [[Paper](https://arxiv.org/pdf/2305.12744)]  

**[AAAI 2022]** Synthetic Disinformation Attacks on Automated Fact Verification Systems [[Paper](https://www.aaai.org/AAAI22Papers/AAAI-11986.DuY.pdf)]  

**[AAAI 2022]** LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/21291)]  

**[SIGIR 2022]** GERE: Generative Evidence Retrieval for Fact Verification [[Paper](https://arxiv.org/pdf/2204.05511.pdf)]  

**[WWW 2022]** EvidenceNet: Evidence Fusion Network for Fact Verification [[Paper](https://dl.acm.org/doi/abs/10.1145/3485447.3512135)]  

**[ACL 2021]** Zero-shot Fact Verification by Claim Generation [[Paper](https://arxiv.org/pdf/2105.14682.pdf)]  

**[ACL 2021]** Unified Dual-view Cognitive Model for Interpretable Claim Verification [[Paper](https://arxiv.org/pdf/2105.09567.pdf)]  

**[ACL 2021]** Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification [[Paper](https://arxiv.org/pdf/2106.01191.pdf)]  

**[ACL 2021]** Structurizing Misinformation Stories via Rationalizing Fact-Checks [[Paper](https://aclanthology.org/2021.acl-long.51.pdf)]  

**[ACL 2021]** Exploring Listwise Evidence Reasoning with T5 for Fact Verification [[Paper](https://aclanthology.org/2021.acl-short.51.pdf)]  

**[ACL 2021]** Evidence-based Factual Error Correction [[Paper](https://aclanthology.org/2021.acl-long.256.pdf)]  

**[ACL Findings 2021]** Strong and Light Baseline Models for Fact-Checking Joint Inference [[Paper](https://aclanthology.org/2021.findings-acl.426.pdf)]  

**[ACL Findings 2021]** A Multi-Level Attention Model for Evidence-Based Fact Checking [[Paper](https://arxiv.org/pdf/2106.00950.pdf)]  

**[CIKM 2021]** CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models [[Paper](https://arxiv.org/pdf/2109.15107.pdf)]  

**[EMNLP 2021]** Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification [[Paper](https://aclanthology.org/2021.emnlp-main.558.pdf?ref=https://githubhelp.com)]  

**[NAACL 2021]** Towards Few-Shot Fact-Checking via Perplexity [[Paper](https://arxiv.org/pdf/2103.09535.pdf)]  

**[NAACL 2021]** How Robust are Fact Checking Systems on Colloquial Claims? [[Paper](https://aclanthology.org/2021.naacl-main.121.pdf)]  



### LLM-based Methods

**[ACL 2024]** Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments [[Paper](https://arxiv.org/pdf/2406.09815)]  

**[NAACL 2024]** Complex Claim Verification with Evidence Retrieved in the Wild [[Paper](https://arxiv.org/pdf/2305.11859)]  

**[NAACL 2024]** Language Models Hallucinate, but May Excel at Fact Verification [[Paper](https://arxiv.org/pdf/2310.14564)]  

**[TACL 2024]** JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims [[Paper](https://arxiv.org/pdf/2401.08026.pdf)]  

**[Arxiv 2024]** Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate [[Paper](https://arxiv.org/pdf/2402.07401)]  

**[EMNLP Findings 2023]** Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models [[Paper](https://aclanthology.org/2023.findings-emnlp.416.pdf)]  

**[AACL 2023]** Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method [[Paper](https://arxiv.org/pdf/2310.00305)]  

**[Arxiv 2023]** Are Large Language Models Good Fact Checkers: A Preliminary Study [[Paper](https://arxiv.org/pdf/2311.17355)]  



### Multimodal Methods

**[Arxiv 2024]** Multimodal Large Language Models to Support Real-World Fact-Checking [[Paper](https://arxiv.org/pdf/2403.03627)]  

**[MM 2023]** ECENet: Explainable and Context-Enhanced Network for Multi-modal Fact Verification [[Paper](https://doi.org/10.1145/3581783.3612183)]  



### Multi-hop Fact Verification

**[AAAI 2023]** Exploring Faithful Rationale for Multi-Hop Fact Verification via Salience-Aware Graph Learning [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/26591/26363)]  

**[EMNLP 2023]** EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification [[Paper](https://aclanthology.org/2023.emnlp-main.826.pdf)]  

**[Arxiv 2023]** Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification [[Paper](https://arxiv.org/pdf/2305.09400)]  

  

### Previously Fact-check



**[NAACL 2022]** The Role of Context in Detecting Previously Fact-Checked Claims [[Paper](https://arxiv.org/pdf/2104.07423.pdf)]  

**[ACL 2021]** Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims [[Paper](https://arxiv.org/pdf/2112.10322.pdf)]  

**[ACL 2021]** Claim Matching Beyond English to Scale Global Fact-Checking [[Paper](https://arxiv.org/pdf/2106.00853.pdf)]  

**[ACL 2020]** That is a Known Lie: Detecting Previously Fact-Checked Claims [[Paper](https://arxiv.org/pdf/2005.06058.pdf)]  

**[EMNLP 2020]** Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News [[Paper](https://arxiv.org/pdf/2010.03159.pdf)]  



### New Datasets



**[ACL 2022]** FAVIQ: FAct Verification from Information-seeking Questions [[Paper](https://arxiv.org/pdf/2107.02153.pdf)]  

**[ACL 2022]** Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines [[Paper](https://arxiv.org/pdf/2104.08790.pdf)]  

**[ACL 2021]** COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic [[Paper](https://arxiv.org/pdf/2106.03794.pdf)]  



---



## Rumor Detection



### Supervised Methods

**[WWW 2024]** Message Injection Attack on Rumor Detection under the Black-Box Evasion Setting Using Large Language Model [[Paper](https://dl.acm.org/doi/pdf/10.1145/3589334.3648139)]  

**[KDD 2023]** Rumor Detection with Diverse Counterfactual Evidence [[Paper](https://dl.acm.org/doi/pdf/10.1145/3580305.3599494)]  

**[AAAI 2023]** Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/25651/25423)]  

**[AAAI 2022]** DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media [[Paper](https://www.aaai.org/AAAI22Papers/AAAI-6370.SunM.pdf)]  

**[COLING 2022]** A Progressive Framework for Role-Aware Rumor Resolution [[Paper](https://aclanthology.org/2022.coling-1.242.pdf)]  

**[COLING 2022]** Social Bot-Aware Graph Neural Network for Early Rumor Detection [[Paper](https://aclanthology.org/2022.coling-1.580.pdf)]  

**[NAACL 2022]** Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning [[Paper](https://arxiv.org/pdf/2204.08143.pdf)]  

**[WWW 2022]** Rumor Detection on Social Media with Graph Adversarial Contrastive Learning [[Paper](https://web.archive.org/web/20220505023214id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3511999)]  

**[WWW 2022]** Detecting False Rumors from Retweet Dynamics on Social Media [[Paper](https://arxiv.org/pdf/2201.13103.pdf)]  

**[ACL Findings 2021]** Adversary-Aware Rumor Detection [[Paper](https://aclanthology.org/2021.findings-acl.118.pdf)]  

**[ACL Findings 2021]** Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection [[Paper](https://aclanthology.org/2021.findings-acl.63.pdf)]  

**[EMNLP 2021]** Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks [[Paper](https://aclanthology.org/2021.emnlp-main.786.pdf?ref=https://githubhelp.com)]  

**[EMNLP 2021]** STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media [[Paper](https://aclanthology.org/2021.emnlp-main.269.pdf)]  

**[AAAI 2020]** Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/5393)]  

**[COLING 2020]** Debunking Rumors on Twitter with Tree Transformer [[Paper](https://aclanthology.org/2020.coling-main.476.pdf?ref=https://githubhelp.com)]  

**[ACL 2018]** Rumor Detection on Twitter with Tree-structured Recursive Neural Networks [[Paper](https://aclanthology.org/P18-1184.pdf)]  



#### with Unreliable Propagations



**[ACL 2021]** Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection [[Paper](https://aclanthology.org/2021.acl-long.297.pdf)]  

**[AAAI 2020]** Interpretable Rumor Detection in Microblogs by Attending to User Interactions [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/6405)]  



#### with Temporal Features



**[COLING 2022]** Continually Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning [[Paper](https://aclanthology.org/2022.coling-1.268.pdf)]  

**[NAACL 2022]** Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset [[Paper](https://aclanthology.org/2022.naacl-main.302.pdf)]  

**[WWW 2021]** Rumor Detection with Field of Linear and Non-Linear Propagation [[Paper](https://dl.acm.org/doi/abs/10.1145/3442381.3450016)]  

**[EMNLP 2020]** A State-independent and Time-evolving Network for Early Rumor Detection in Social Media [[Paper](https://aclanthology.org/2020.emnlp-main.727.pdf)]  



#### with User Profile



**[NAACL 2022]** DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks [[Paper](https://aclanthology.org/2022.naacl-main.364.pdf)]  



#### with Data Augmentation



**[SIGIR ShortPaper 2021]** Rumor Detection on Social Media with Event Augmentations [[Paper](https://www.researchgate.net/profile/Zhenyu-He-5/publication/353188656_Rumor_Detection_on_Social_Media_with_Event_Augmentations/links/62e8f9a83c0ea87887765e3d/Rumor-Detection-on-Social-Media-with-Event-Augmentations.pdf)]  

**[ICLR 2019]** Data Augmentation for Rumor Detection using Context-sensitive Neural Language Model with Large-scale Credibility [[Paper](https://openreview.net/pdf?id=SyxCysRNdV)]  



### Multi-modal Methods



**[IJCAI 2022]** MFAN: Multi-modal Feature-enhanced Attention Networks for Rumor Detection [[Paper](https://www.ijcai.org/proceedings/2022/0335.pdf)]  

**[EMNLP 2021]** Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection [[Paper](https://aclanthology.org/2021.findings-emnlp.122.pdf)]  

**[MM 2019]** Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection [[Paper](https://dl.acm.org/doi/abs/10.1145/3343031.3350850)]  

**[MM 2017]** Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs [[Paper](https://dl.acm.org/doi/abs/10.1145/3123266.3123454)]  



### Joint Stance & Rumor Detection



**[SIGIR 2022]** A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning [[Paper](https://arxiv.org/pdf/2204.02626.pdf)]  

**[ACL ShortPaper 2019]** Rumor Detection By Exploiting User Credibility Information,  Attention and Multi-task Learning [[Paper](https://aclanthology.org/P19-1113.pdf)]  

**[COLING 2018]** All-in-one: Multi-task Learning for Rumour Verification [[Paper](https://arxiv.org/pdf/1806.03713.pdf)]  

**[WWW 2018]** Detect Rumor and Stance Jointly by Neural Multi-task Learning [[Paper](https://dl.acm.org/doi/pdf/10.1145/3184558.3188729)]  



---



## Summarizations of FND



#### Social Media-based Fake News Detection and Rumor Detection

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**



#### Context-based Fake News 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)**



---



## Famous Chinese Researchers in FND

- [Juan Cao](https://people.ucas.ac.cn/~caojuan), Institute of Computing Technology, Chinese Academy of Sciences

- [Jing Ma](https://majingcuhk.github.io/), Department of Computer Science, Hong Kong Baptist University

- [Huan Liu](https://www.public.asu.edu/~huanliu/), Ira A. Fulton Schools of Engineering, Arizona State University

- [Kai Shu](http://www.cs.iit.edu/~kshu/index.html), Department of Computer Science, Illinois Institute of Technology

- [Songlin Hu](https://people.ucas.edu.cn/~husonglin), Institute of Information Engineering, Chinese Academy of Sciences

- [Linmei Hu](https://scholar.google.com/citations?user=OphdKw8AAAAJ&hl=zh-CN&oi=ao), Beijing Institute of Technology