Data-driven Understanding of the Disinformation Epidemic

Yang Zhang

News

Schedule of presentation is online

Written on 03.05.24 by Xinyue Shen

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… Read more

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, Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities


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. Rustam Nurullayev, DISARM: Detecting the Victims Targeted by Harmful Memes
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. Amirhossein Saemi, Zoom Out and Observe: News Environment Perception for Fake News Detection


 

Paper list is online

Written on 30.04.24 by Xinyue Shen

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… Read more

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

Data-driven Understanding of the Disinformation Epidemic

 

Arguably, one of the greatest inventions of humanity is the Web. Despite the fact it revolutionized our lives, the Web has also introduced or amplified a set of several social issues like the spread of disinformation and hateful content to a large number of people.

In this seminar, we will look into research that focuses on extracting insights from large corpus of data with the goal to understand emerging socio-technical issues on the Web such as the dissemination of disinformation and hateful content. We will read, present, and discuss papers that follow a data-driven approach to analyze large-scale datasets across several axes to study the multi-faceted aspects of emerging issues like disinformation.

During this seminar, the participants will have the opportunity to learn about state-of-the-art techniques and tools that are used for large-scale processing, including, but not limited to, statistical techniques, machine learning, image analysis, and natural language processing techniques.

Note,  please do not participate in the seminar if you didn’t get a spot via the seminar assignment system.

Logistics

Location: CISPA room 0.02 (Stuhlsatzenhaus 5, 66123)

Lecturer: Yang Zhang

TAs: Xinyue Shen, Yiting Qu, Ziqing Yang, Yixin Wu

Contact: xinyue.shen@cispa.de

Time: every Wednesday from 4 PM to 5 PM

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