Data-driven Understanding of the Disinformation Epidemic

Dear all,

After receiving your responses, we have arranged a schedule for you to give the presentations (see it at the end of this message).

The presentation starts on 8th May. Every Wednesday from 4 pm to 5 pm, we will have two presenters introduce their preferred papers.

See you on the 8th of May :)

Best,
Xinyue

---

08.05.2024:
1. Chi Cui, “It’s Stressful Having All These Phones”: Investigating Sex Workers’ Safety Goals, Risks, and Practices Online
2. Moritz Leonhard Hübner, TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities
 

15.05.2024:
3. Venkata Udhay Kiran Pabbathi Sathyanarayana, Aunties, Strangers, and the FBI: Online Privacy Concerns and Experiences of Muslim-American Women
4. Leonard Nicolas Tran, Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots

22.05.2024: 
5. Lena Miriam Pelz, “Dummy Grandpa, Do You Know Anything?”: Identifying and Characterizing Ad Hominem Fallacy Usage in the Wild
6. Gleb Rostanin, You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content
 

29.05.2024:
7. Amirhossein Saemi, Zoom Out and Observe: News Environment Perception for Fake News Detection
8. Junaed Tariq, Understanding and Detecting Hateful Content Using Contrastive Learning

 

05.06.2024: 
9. Abhishek Ganesh Shinde, RIATIG: Reliable and Imperceptible Adversarial Text-To-Image Generation With Natural Prompts
10. Pavan Raviteja Upadhyayula, Misinformation: Susceptibility, Spread, and Interventions to Immunize the Public

12.06.2024: 
11. Carlos Zender Fernandez, Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites
12. Justin Steuer, Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

19.06.2024:
13. Raj Piyushbhai Sheth, DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks
14. Kamila Szewczyk, Can LLM-Generated Misinformation Be Detected

26.06.2024:
15. Niklas Lohmann, On the Risk of Misinformation Pollution with Large Language Models
16. Rustam Nurullayev, DISARM: Detecting the Victims Targeted by Harmful Memes

Dear all,

The paper list is online, please select three papers (ranked by preference) and send them to Xinyue Shen (xinyue.shen@cispa.de) by 10 am on 02.05.2024.

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

The assignment will be informed at 2 pm on 02.05.2024.

Best,

Xinyue

---

Paper list

1. Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models

2. Aunties, Strangers, and the FBI: Online Privacy Concerns and Experiences of Muslim-American Women

3. “It’s Stressful Having All These Phones”: Investigating Sex Workers’ Safety Goals, Risks, and Practices Online

4. TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities

5. On Xing Tian and the Perseverance of Anti-China Sentiment Online

6. “Dummy Grandpa, Do You Know Anything?”: Identifying and Characterizing Ad Hominem Fallacy Usage in the Wild

7. On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning

8. Understanding and Detecting Hateful Content Using Contrastive Learning

9. Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models

10. DISARM: Detecting the Victims Targeted by Harmful Memes

11. Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites

12. DeepPhish: Understanding User Trust Towards Artificially Generated Profiles in Online Social Networks

13. Open-Domain, Content-Based, Multi-Modal Fact-Checking of Out-Of-Context Images via Online Resources

14. Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

15. Misinformation: Susceptibility, Spread, and Interventions to Immunize the Public

16. Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots

17. Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

18. Toxicity in ChatGPT: Analyzing Persona-assigned Language Models

19. A Holistic Approach to Undesired Content Detection in the Real World

20. You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content

21. Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

22. Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

23. SneakyPrompt: Jailbreaking Text-to-image Generative Models

24. On the Proactive Generation of Unsafe Images From Text-to-Image Models Using Benign Prompts

25. RIATIG: Reliable and Imperceptible Adversarial Text-To-Image Generation With Natural Prompts

26. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions

27. Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection

28. Can LLM-Generated Misinformation Be Detected

29. On the Risk of Misinformation Pollution With Large Language Models

30. Zoom Out and Observe: News Environment Perception for Fake News Detection