Important Dates

This tutorial takes place on Monday, June 23, as part of ICWSM 2025 in Copenhagen. 

🕐 08:00 AM - 12:00 PM

📍 Room 2.1.042 (AAU)

The required materials are posted below. If you have any questions, feel free to reach out to the organizers. Otherwise, see you soon!

Abstract

As AI-generated content becomes more prevalent, understanding its role within the broader misinformation landscape is critical. The widespread proliferation of misinformation in combination with the rise of AI technologies poses challenges across domains: Concerns persist that, for example, Large Language Models (LLMs) or deepfake systems have a negative impact on the creation and amplification of false or misleading information. While there are debates within the research community on the extent of AI influence on misinformation development, the challenges posed by misinformation are amplified as social media platforms increasingly dismantle traditional guardrails like fact-checking. These shifts demand interdisciplinary research to explore not only how AI contributes to the spread of misinformation but also how it can serve as a tool to better understand and combat it. Situating AI-generated misinformation within the wider context of existing dynamics highlights the urgency of addressing its impact across domains, both in science and politics, particularly as societal polarization deepens. 

Participants will gain hands-on experience analyzing misinformation-related datasets using natural language processing and network analysis. The tutorial emphasizes practical applications by providing coding exercises in a Jupyter notebook environment for detecting and simulating the spread of misinformation.

Participant Prerequisites

This tutorial is open to students, researchers, and practitioners who want to explore computational strategies for mitigating misinformation, particularly in the context of the growing influence of AI technologies. Prerequisites include a foundational understanding of text classification methods and language model architectures and proficiency in using Jupyter notebooks, including familiarity with Python libraries such as pandas, scikit-learn, or similar tools.

Tutorial Organizers

Tutorial materials can be accessed here.