Desinformationskampagnen beheben durch Offenlegung der Faktoren und Stilmittel

Timing: 01/2022 - 12/2024
Funding: Bundesministerium für Bildung und Forschung

About the Project: Addressing Disinformation Campaigns by Disclosing Factors and Techniques. Disinformation campaigns, where misleading information is deliberately spread on a large scale, have become a central threat to the political process and social cohesion. They can influence elections and incite people to engage in self-destructive or even terrorist behavior. In addition to political polarization and opinion division, they also promote other harmful societal phenomena, such as conspiracy theories.

The project DeFaktS, funded by the Federal Ministry of Education and Research (BMBF), follows a comprehensive approach to researching and combating disinformation. For this purpose, an Artificial Intelligence (AI) is trained based on extracted messages from suspicious social media and messenger groups to recognize factors and techniques characteristic of disinformation. The trained AI then forms a component for an XAI (Explainable Artificial Intelligence), which is intended to inform and warn users of online platforms about the potential occurrence of disinformation in a transparent manner.

Another goal of DeFaktS is to make the XAI component accessible to third parties through the development of an application programming interface (API), thereby contributing to a solution that allows online platforms to be moderated as automatically as possible.

During the project period from January 2022 to December 2024, the FZI Research Center for Information Technology leads the consortium with project partners Murmuras UG, Liquid Democracy e.V., and Philipps University of Marburg. DeFaktS is funded by the Federal Ministry of Education and Research.

Publications: 
DeFaktS: A German Dataset for Fine-Grained Disinformation Detection through Social Media Framing, S Ashraf, I Bezzaoui, I Andone, A Markowetz, J Fegert, L Flek, Proceedings of the 2024 Joint International Conference on Computational …
Pitfalls of Conversational LLMs on News Debiasing, I Baris Schlicht, D Altiok, M Taouk, L Flek, arXiv e-prints, arXiv: 2404.06488