Data-driven Understanding of the Disinformation Epidemic (DUDE)

Dear all,

After receiving your responses, we have arranged a schedule for you to give the presentations.

The presentation starts on 28th April and continues every Tuesday from 4 pm to 5 pm at CISPA E 9 1 0.07. Two presenters will introduce their preferred papers.

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28.04.2026    
1    Syed Usfar Wasim    HATEDAY: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter
2    Ahmed Waleed Ahmed Badawy Shora    Content Moderation and Hate Speech on Alternative Platforms: A Case Study of BitChute
        
05.05.2026    
3    Mohamed Bilal Jan Mahaboob Jan    Investigating Moderation Challenges to Combating Hate and Harassment: The Case of Mod-Admin Power Dynamics and Feature Misuse on Reddit
4    Karen Jane Concessao    HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
        
12.05.2026    
5    Usaid Bhirya    Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoder
6    Muhammad Fayez Shahid    You Know What I Meme: Enhancing People’s Understanding and Awareness of Hateful Memes Using Crowdsourced Explanations
        
19.05.2026    
7    Prachi Sajwan    Exploring fake news awareness and trust in the age of social media among university student TikTok users
8    Elias Sheikh    A Representative Study on Human Detection of Artificially Generated Media Across Countries
        
26.05.2026    
9    Aakase Augustine    Better Be Computer or I'm Dumb: A Large-Scale Evaluation of Humans as Audio Deepfake Detectors
10    Robert Alexander Rabbe    Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection
        
02.06.2026    
11    Jonas Walfort    Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom
12    Rishika Kumari    Can LLM-Generated Misinformation Be Detected?

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Best,
Ziqing

Dear all,

The paper list is online. Please select three papers (ranked by preference) and send them to Ziqing Yang (ziqing.yang@cispa.de) by 17.04.2026.

Note that the assignment will be based on the first-come, first-served principle.

The assignment will be informed by 20.04.2026.

Best,

Ziqing

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Paper List

1. HATEDAY: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter
2. Content Moderation and Hate Speech on Alternative Platforms: A Case Study of BitChute
3. Investigating Moderation Challenges to Combating Hate and Harassment: The Case of Mod-Admin Power Dynamics and Feature Misuse on Reddit
4. Understanding the Security and Privacy Implications of Online Toxic Content on Refugees
5. Supporting Human Raters with the Detection of Harmful Content using Large Language Models
6. HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
7. Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
8. You Know What I Meme: Enhancing People’s Understanding and Awareness of Hateful Memes Using Crowdsourced Explanations
9. Exploring fake news awareness and trust in the age of social media among university student TikTok users
10. A Representative Study on Human Detection of Artificially Generated Media Across Countries
11. "Better Be Computer or I'm Dumb": A Large-Scale Evaluation of Humans as Audio Deepfake Detectors
12. Combating misinformation in the age of LLMs: Opportunities and challenges
13. Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites
14. You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content.
15. Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoder
16. from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors.
17. Toxicity Detection for Free.
18. Robust Adaptation of Large Multimodal Models for  Augmented Hateful Meme Detection
19. MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
20. From Meme to Threat: On the Hateful Meme Understanding and Induced Hateful Content Generation in Open-Source Vision Language Models
21. On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
22. SneakyPrompt: Jailbreaking Text-to-image Generative Models
23. DRES: Fake news detection by dynamic representation and ensemble selection
24. Can Large Language Models Identify Authorship?
25. Adapting fake news detection to the era of large language models
26. Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
27. On Fake News Detection with LLM Enhanced Semantics Mining
28. Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection
29. Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling
30. Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom
31. Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
32. Can LLM-Generated Misinformation Be Detected?