Category: Thesis
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Diversity in News Recommendations- Towards a Broader Perspective on Diversity-Aware-Recommender-Systems and Their Influence on Users
Author: Linda Basso Supervisor: Irina Nalis-Neuner, Co-Supervisor: Julia Neidhardt Abstract All of us encounter recommender systems (RS) regularly by using social media, google, online-shopping, streaming music, reading the news online, etc. The interaction with these information filtering systems is part of our everyday lives. Thus, they have an influence on their users’ behavior, their decision…
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Exploring group fairness in news media recommendations: Algorithms, metrics, and grouping
Author: Blake Huebner Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although fairness and diversity metrics are widely discussed,…
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COVID-19 and Populism in Austrian News User Comments – A Machine Learning Approach
Author: Ahmadou Wagne Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract The COVID-19 pandemic and the resulting government measures have triggered a wave of protests and demonstrations in Austria. We saw some protestors resorting to populist rhetoric to express their dissatisfaction, which in some cases, led to anti-democratic tendencies. Populist talking points have not been…