Who? Research Project on «The Role of News Recommender Systems in Digital Democracies» Sina Blassnig1, Edina Strikovic2, Eliza Mitova1, Aleksandra Urman3, Anikó Hannák3, Frank Esser1, & Claes de Vreese2, 1 Department of Communication and Media Research, University of Zürich 2 Amsterdam School of Communication Research, University of Amsterdam 3 Department of Informatics, University of Zürich Contact: [email protected] 25.11.2022 Page 1

Why? 25.11.2022 Page 2 – Changing user behavior – Increasing automation and personalization of news Source: Digital News Report 2022, Data for Switzerland

What are News Recommender Systems (NRS)? – Algorithms that can make automated and/or personalized recommendations based on metadata, past behavior, ratings of similar users, popularity, and/or content-specific features (Ricci et al., 2011) – NRS can be used on the frontpage of news media websites, below articles, in special sections, for newsletters, etc. 25.11.2022 Page 3

What is the problem? – Good NRS are expensive, laborious and complicated. – That’s why media still often focus only on: – Topic similarity – Popularity 25.11.2022 Page 4

What is the Challenge? 1) Analyze the problem: – What gets left behind if we focus on popularity-based recommenders? what categories, e.g., topics, regions, actors, formats etc. are over- or underrepresented 2) Provide a solution: – What would need to be pushed in a diversity-maximizing recommender? what categories, e.g., topics, regions, actors, formats etc. – Can this be done in a transparent way? E.g., by showing why something is recommended 25.11.2022 Page 5

Potential Resources & Needed Data Needed: − Content archive (articles, videos, audio) − Aggregated user data (e.g., likes, clicks, views, time spent) Available: - SRG APIs: https://developer.srgssr.ch/apis/ - Others? 25.11.2022 Page 6

Contact Sina BlassnigDepartment of Communication and Media Research, University of Zürich Contact: [email protected]  Slack 25.11.2022 Page 7