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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.

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README 

        



## Fake News Detection



An awesome paper list of **fake news detection (FND)** and **rumor detection** with ![](https://img.shields.io/badge/139-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](#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

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

- 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://watermark.silverchair.com/coli_a_00380.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAq4wggKqBgkqhkiG9w0BBwagggKbMIIClwIBADCCApAGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMxz9Xg1SndcEbQDYRAgEQgIICYezKOBSVkeblU0UiMgQBFTxwWxvVxyHMsoZNjebxXy_s7hVI8uyU1oxQJ0CFP1zIZd65qql15yVtv5CEq3RHl7TKSCYtFPWhMF4t-1jQaugAnyONxxeNuzPqHSrswxpjDG8HveRLQUiwoftHtfwjc0xUKG7pgHOpjXTWslc2XcQLv4HZ_krec_fLwygsTymj7jkzhn2v2aZdrpNXXzInEajuZA6bNeVhOfUmH2RaKMeRtrvVgXz6hiGn-zvZq2bcBWdHRueLpJY2vakrzJnQf42CghuRVvzxP2Hj-qfBfb08YnQI3lwmbERyn4GiKxAfQzyEoX_tkY8nqwpOm8t5wA-tTHW_AYKXQsoQMp1j2Q8wShdDRVQMnGyWLgxNR5WOJTgTvmcnr6D1tQTJhd_ilxUPCvNd8RnD4fU_7jWXeeEDtb5hQi45zKVUp9SropLnacTuOiQN4xY1saSH8EQCdNHJ79X9QZ2Ii7NGZrVm4ZCVcpN4DqHgR3WFeHPKZrTyT_6fluW-Mc69SpuIhu7nRLgavhAbyC8UAHS_Krk6vhch1GWIMPXeWyfo66jNJ_jZUUY8lEnoNfsfuyARpqb9x0IEseo_5WeHpR2SrQpWGlWxWBO8Twwi0nJgp9nlv4Ig7a4LAG66UXxRbzQs0kXENqOJM_qRVCIAKF_JDgKhXnk8Xoq15o-3fTdUeYlv-7mS_4XpA0f8l9nVRmq2GMwiNe41JuA1yV5nggN91T6bEC7mq9Vnc9x9B6uXji2tOT6TD7cmh-2XQl7CIbBvRfyLJpieHR0vFBydp9mnHXVuTT8v1Q)]



#### 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

- 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)]



---



### Social Media-based FND



- AAAI 2023, HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection [[Paper](https://www.atailab.cn/seminar2023Spring/pdf/2023_AAAI_Jointly%20Learning%20of%20Global%20and%20Local%20User%20Spreading%20Behavior%20for%20Fake%20News%20Early%20Detection.pdf)]

- 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

- 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)]

- 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

- 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)]

- 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



- 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)]



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## 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)**



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## 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