# Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models

arXiv: https://arxiv.org/abs/2405.16631 · HTML: https://mlsanity.com/paper/2405.16631/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 74 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation](https://mlsanity.com/paper/2312.09085/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation](https://mlsanity.com/paper/2310.15515/) (2023)
- [From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News](https://mlsanity.com/paper/2403.09498/) (2024)
- [Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom](https://mlsanity.com/paper/2405.03371/) (2024)
- [Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues](https://mlsanity.com/paper/2108.10509/) (2021)
- [FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms](https://mlsanity.com/paper/2211.10973/) (2022)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation](https://mlsanity.com/paper/2402.11943/) (2024)
- [MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models](https://mlsanity.com/paper/2009.01048/) (2020)
- [Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News](https://mlsanity.com/paper/2004.01732/) (2020)
- [Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection](https://mlsanity.com/paper/2306.14728/) (2023)
- [Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation](https://mlsanity.com/paper/1909.11974/) (2019)
- [DEEM: Dynamic Experienced Expert Modeling for Stance Detection](https://mlsanity.com/paper/2402.15264/) (2024)
- [Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting](https://mlsanity.com/paper/2310.10429/) (2023)
- [Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting](https://mlsanity.com/paper/2310.13297/) (2023)
- [Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One](https://mlsanity.com/paper/2402.12150/) (2024)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)
- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction](https://mlsanity.com/paper/2402.18950/) (2024)
- [Exploring news intent and its application: A theory-driven approach](https://mlsanity.com/paper/2312.16490/) (2023)
- [Adaptive Interaction Fusion Networks for Fake News Detection](https://mlsanity.com/paper/2004.10009/) (2020)

## Built on by (most cited first, 17 of 17)

- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents](https://mlsanity.com/paper/2507.19090/) (2025)
- [Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator](https://mlsanity.com/paper/2504.04076/) (2025)
- [Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate](https://mlsanity.com/paper/2412.04859/) (2024)
- [Robust Misinformation Detection by Visiting Potential Commonsense Conflict](https://mlsanity.com/paper/2504.21604/) (2025)
- [From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild](https://mlsanity.com/paper/2603.25423/) (2026)
- [Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection](https://mlsanity.com/paper/2509.01660/) (2025)
- [Enhancing Fake News Video Detection via LLM-Driven Creative Process Simulation](https://mlsanity.com/paper/2510.04024/) (2025)
- [Exploring Modality Disruption in Multimodal Fake News Detection](https://mlsanity.com/paper/2504.09154/) (2025)
- [Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection](https://mlsanity.com/paper/2605.19285/) (2026)
- [Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations](https://mlsanity.com/paper/2508.03420/) (2025)
- [SARC: Sentiment-Augmented Deep Role Clustering for Fake News Detection](https://mlsanity.com/paper/2511.04692/) (2025)
- [Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling](https://mlsanity.com/paper/2602.01726/) (2026)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)
- [A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM](https://mlsanity.com/paper/2604.06666/) (2026)
- [An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detection](https://mlsanity.com/paper/2605.02259/) (2026)
- [Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News](https://mlsanity.com/paper/2610.08835/) (2026)

## Abstract (arXiv)

Fake news detection plays a crucial role in protecting social media users and maintaining a healthy news ecosystem. Among existing works, comment-based fake news detection methods are empirically shown as promising because comments could reflect users' opinions, stances, and emotions and deepen models' understanding of fake news. Unfortunately, due to exposure bias and users' different willingness to comment, it is not easy to obtain diverse comments in reality, especially for early detection scenarios. Without obtaining the comments from the ``silent'' users, the perceived opinions may be incomplete, subsequently affecting news veracity judgment. In this paper, we explore the possibility of finding an alternative source of comments to guarantee the availability of diverse comments, especially those from silent users. Specifically, we propose to adopt large language models (LLMs) as a user simulator and comment generator, and design GenFEND, a generated feedback-enhanced detection framework, which generates comments by prompting LLMs with diverse user profiles and aggregating generated comments from multiple subpopulation groups. Experiments demonstrate the effectiveness of GenFEND and further analysis shows that the generated comments cover more diverse users and could even be more effective than actual comments.