Category: Master Thesis

  • Exploration of Content-Based Cross-Domain Podcast Recommender Systems

    Author: Matthias Hofmaier Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Podcasts have become a popular medium in the last decade. The huge amount of data available motivates research on podcast recommender systems to make this data accessible to users. Since interaction-based datasets for podcasts are only available to the large streaming providers, content-based methods…

  • Classification of users in an online news forum – a data analysis of user types and user interaction in the online forum of an Austrian newspaper

    Author: Felix Scholz Supervisor: Hannes Werthner, Co-Supervisor: Julia Neidhardt Abstract Online news forums provide a longstanding way for the exchange of opinions with other users. These often-large user bases consist of a cross-section of all people with various opinions and motivations. By classifying recurring types of behaviors common in such a context, an effort is…

  • Network Analysis on the Austrian Media Corpus: Examining measures of co-occurrence between entities in Austrian media

    Author: Gabriel Grill Supervisor: Hannes Werthner, Co-Supervisor: Julia Neidhardt Abstract The quantitative study of news media can provide insights into reporting patterns and enable public discourse. Print media is essential in democratic societies, so its study remains important. This thesis examines Austrian reporting using network-based methods and unpacks the suitability of such an approach for…

  • 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,…

  • 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…

  • Merging the Gap Between Automated and Human Centered Usability Testing

    Author: Can Özgür Yilmaz Supervisor: Allan Hanbury, Co-Supervisor: Julia Neidhardt Abstract In today’s world, usability testing is an essential aspect of the software developmentprocess. Conventional usability testing however is very time and resource consuming,because there needs to be a real person observing the user and trying to discover usabilityproblems from their observations. As a solution…