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 Monday from 4 pm to 5 pm at CISPA E 9 1 0.07. Two presenters will introduce their preferred papers.

 

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28.04.2025

• 1, Schmitt Francesco Georg, Dummy Grandpa, do you know anything?: Identifying and Characterizing Ad Hominem Fallacy Usage in the Wild
• 2, Franke Konstantin Maximilian, TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Reddit

05.05.2025

• 3, Bakowsky Ivo Maximilian, TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities
• 4, Lodhi Muhammad Azeem, Aunties, Strangers, and the FBI: Online Privacy Concerns and Experiences of Muslim-American Women

12.05.2025

• 5, Farooqui Suffiyan Ul Hassan, HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
• 6, Hussain Bakhtiar, HOT ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media

19.05.2025

• 7, Siddiqui Ahmed Faraz, Toxicity in ChatGPT: Analyzing Persona-Assigned Language Models
• 8, Mehta Gautam, DISARM: Detecting the Victims Targeted by Harmful Memes

26.05.2025

• 9, Bhawkar Khushi Ankush, You Know What I Meme: Enhancing People’s Understanding and Awareness of Hateful Memes Using Crowdsourced Explanations
• 10, Munduri Lavanya Ratna Sirisha, Understanding and Detecting Hateful Content Using Contrastive Learning

02.06.2025

• 11, Amin Bilal, On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning
• 12, Tianze Chang, Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

16.06.2025

• 13, Hameed Osama, SneakyPrompt: Jailbreaking Text-to-image Generative Models
• 14, Knop Vincent, Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

23.06.2025

• 15, Aziz Ali Azlan, DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks
• 16, Ahmed Hassan, Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites

30.06.2025

• 17, Marciu Vlad Mihai, Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions
• 18, Basu Sambit, Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

07.07.2025

• 19, Xinyu Zhang, Can LLM-Generated Misinformation Be Detected?
• 20, Ravichandran Kaviya, Disinformation Detection:  An Evolving Challenge in the Age of LLMs

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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 10 a.m. on 18.04.2025.

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

The assignment will be informed at 2 pm on 21.04.2025.

Best,

Ziqing

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Paper List

1. HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
2. Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models
3. Aunties, Strangers, and the FBI: Online Privacy Concerns and Experiences of Muslim-American Women
4. "HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media
5. TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities
6. TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Reddit
7. "Dummy Grandpa, do you know anything?": Identifying and Characterizing Ad Hominem Fallacy Usage in the Wild
8. You Know What I Meme: Enhancing People’s Understanding and Awareness of Hateful Memes Using Crowdsourced Explanations
9. On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning
10. Understanding and Detecting Hateful Content Using Contrastive Learning
11. Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
12. DISARM: Detecting the Victims Targeted by Harmful Memes
13. Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites
14. DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks
15. Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources
16. Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter
17. Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots
18. Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
19. Toxicity in ChatGPT: Analyzing Persona-Assigned Language Models
20. A Holistic Approach to Undesired Content Detection in the Real World
21. You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content
22. From Meme to Threat: On the Hateful Meme Understanding and Induced Hateful Content Generation in Open-Source Vision Language Models
23. On the proactive generation of unsafe images from text-to-image models using benign prompts
24. SneakyPrompt: Jailbreaking Text-to-image Generative Models
25. Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models
26. Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models
27. RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts
28. Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom
29. Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
30. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions
31. Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
32. Can LLM-Generated Misinformation Be Detected?
33. Disinformation Detection:  An Evolving Challenge in the Age of LLMs
34. On the Risk of Misinformation Pollution with Large Language Models
35. From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News