Digital Humanism 2020ICT20-016

Young People Against Online Hate: Computer-assisted Strategies for Facilitating Citizen-generated Counter Speech

Young People Against Online Hate: Computer-assisted Strategies for Facilitating...
Principal Investigator:
Matthias Zeppelzauer (University of Applied Sciences St. Pölten) (Co-Principal Investigator)
Christiane Atzmüller (University of Vienna) (Co-Principal Investigator)
Ongoing (15.09.2021 – 14.09.2024)
Funding volume:
€ 449,680


Hate-speech (HS) is a massive problem in young people’s digital environments. Common strategies like blocking, reporting, ignoring or deleting have proven to be ineffective, while citizen-generated counter speech (CS), i.e., the mobilization of Internet users to counteract by setting a humanistic sign against HS, appears to be highly promising. However, youth-generated CS hardly occurs. Recent studies by the applicants show that this is mainly grounded in a massive lack of support and skills, and feelings of being lost in the crowd with no hints whether CS was successful or not. Overall, the perceived powerlessness leads to a low attractiveness of youth-generated CS. Thus, we ask: How can we use digital technology to (1) support young counter speakers, (2) make effective CS more visible, and (3) increase the feasibility and attractiveness of CS? Computer-assisted strategies based on automated detection and recommendation of CS is a promising approach. However, the few available studies do not consider youth-specific characteristics, ignore visual content often used by young people, and neglect complex text- and image-based interactions. This project aims at closing these research gaps and provides novel insights on how to better integrate human and societal values into future social media use by fostering youth-generated CS through technical means. We combine methods from social and computer science and use a citizen science approach for close cooperation with young people.

Scientific disciplines: 504016 - Sociology of youth (50%) | 102019 - Machine learning (50%)

We use cookies on our website. Some of them are technically necessary, while others help us to improve this website or provide additional functionalities. Further information