Deep Learning and Statistical Models to Analyse Online Misinformation and Hate Speech Impact on African Youth - Gyimah, Esther; Dake, Delali Kwasi; Mawusi, Confidence; Ofori, Elijah (Journal of Information System and Informatics, 2025)
This study examines the perceptions, behaviour, and digital experiences of African youth in relation to online misinformation and hate speech. Using a large-scale, cross-national survey with 10,005 valid responses, the research relies on both statistical clustering and deep learning-based autoencoder models to group youth together based on their trust in information, concern about misinformation, verification behaviours and platform usage. The dual-method analysis highlights three distinct behavioural and attitudinal clusters of youth, denoting different levels of digital skeptical engagement, exposure, and civic engagement. The findings highlight the heterogeneity within the youth population and emphasize that a one-size-fits-all approach to combating misinformation is insufficient. Notably, youth with high concern also demonstrated strong verification habits, while less engaged clusters exhibited low concern and limited digital resilience. These insights offer a foundation for designing cluster-specific interventions and media literacy strategies that are regionally and behaviourally responsive. This combination advances research through unsupervised deep learning on large social survey data, as well as demonstrating the utility of deep learning in revealing latent behaviours. The implications of this study's findings are timely for educators, policy makers and digital platforms more broadly, that want to foster informed and safe digital participation for African youth. As scalable, data-driven framework is a contribution towards an inclusive digital policy package for varied youth realities that exist in an African context.