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All Journal Journal of Information Systems and Informatics Scientific Journal of Computer Science
Ofori, Elijah
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Computer Science & IT

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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 Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1141

Abstract

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.
Smart Choice: Machine Learning Insight into Factors Influencing Students’ Programme Selection at the Tertiary Institution Yakubu, Abubakar Mahami; Ofori, Elijah
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.397

Abstract

Understanding the factors influencing students’ choice of programme of study is increasingly important for tertiary institutions in Ghana, particularly amid rising enrolment rates and growing competition. While prior studies have applied machine learning to predict academic performance, limited research has examined programme selection behaviour at the senior high school level using mixed-type clustering techniques. This study addresses this gap by applying the K-prototype clustering algorithm and supervised classification models to survey data collected from 1,042 final-year Business and Home Economics students across ten senior high schools in Northern Ghana. The clustering process identified three behavioural segments comprising 423, 382, and 237 students, respectively, with the majority aged 16–20 years. Internal validation metrics indicated modest cluster separation. Subsequent classification modelling using Naïve Bayes, Logistic Regression, Decision Tree (J48), Random Forest, and Support Vector Machine (SVM) showed that SVM achieved the highest predictive performance (Accuracy = 99%) when predicting cluster membership. Key influencing factors included parental education, parental occupation, counselling exposure, socio-cultural beliefs, and peer influence. The findings highlight the need for strengthened, context-sensitive guidance and counselling frameworks at the pre-tertiary level to support informed and independent programme selection decisions.
Co-Authors Dake, Delali Kwasi Gyimah, Esther Mawusi, Confidence Yakubu, Abubakar Mahami