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Using AI to Increase Resilience against Toxicity in Online Entertainment (ToxicAInment)

This project brings together theories of entertainment, visual communication and toxic speech to understand how and why toxicity becomes more permissible when it is masked as entertainment. It deploys AI methods to identify, classify and map toxic entertainment at scale, qualitative methods to study its characteristics, and experimental methods to understand its effects on individual behavior.

Projekt description

Social media have become essential tools for political communication, social interaction and information exchange. But while use of these platforms in general has been associated with a number of positive democratic outcomes, significant concerns have been raised about how video- or image-based platforms like TiKToK, YouTube and Instagram can be used to effectively disseminate toxic, extremist, conspiratorial, and misleading content. Such content has been shown to have detrimental effects on psychological functioning and on citizens’ well-being. This project brings together theories of entertainment, visual communication and toxic speech into a novel and empirically testable theoretical framework. It aims to map the scale, and study the characteristics, of entertainment-based visual toxic content seeking to understand its consequences on user attitudes and behaviors towards it. Its research design relies on AI methods for automated content classification and survey experiments for the uncovering of causal effects of toxic entertainment.

Project team

Prof. Dr. Yannis Theocharis

Professor, Lehrstuhl für Digital Governance | TUM School of Social Sciences and Technology | Technische Universität München

Prof. Dr. Diana Rieger

Professor, Lehrstuhl für Kommunikationswissenschaft | Institut für Kommunikationswissenschaft und Medienforschung | Ludwig-Maximilians-Universität in Munich

Prof. Dr. Carsten Schwemmer

Professor, Computational Social Sciences | Institut für Soziologie | Ludwig-Maximilians-Universität München