MediaFutures

Mid-term evaluation of PhD candidate Khadiga Seddik
2025-04-04 14:00 - 15:00 (Europe/Oslo) | SFI MediaFutures, MCB
On Friday, April 4th, PhD candidate Khadiga Seddik will have her mid-term evaluation.
Main supervisor: Erik Knudsen
Co-Supervisor: Damian Trilling, Alain Starke (Step-in)
External Evaluator: Sole Pera

Title: The Influence of News Recommender technology on Shaping Selective Exposure and Sharing.
Abstract:
Despite the benefits the news recommenders provide, overly personalized news recommendations and too much exposure to like-minded news can pose a threat to democracy by leading to filter bubble, echo-chambers, and political polarization. These negative consequences are not given, but they could depend on conditions and factors under which news recommenders amplify or reduce selective exposure. Many studies argue that news recommenders can be programmed to promote factors that reduce selective exposure because they are programmed by human beings, and they are dependent on the decisions surrounding the implementation and design of the technology. However, programming recommender systems to shape selective exposure is not a straightforward task, as we don’t know which factors the recommenders should be designed to promote, as well as how the recommenders should promote them. In this research project I investigate the heavily debated consequences of news recommender technologies, selective exposure and selective of like-minded news. The aim is to shift the scholarly attention from uncovering whether the current recommenders amplify or reduce selective exposure to understanding the conditions under which recommender systems do so, given that they are designed for that purpose. By doing so, we shift the responsibility for the democratic implications of recommenders from the technology itself to the decisions surrounding the implementation and design.
The project is a part of a larger project, the NEWSREC project (https://www.newsrec.ai). The main objective of NEWSREC project is to study, understand, and assess the precise conditions under which algorithmic news recommenders have positive or negative effects on the democratic role of the news media by focusing on both the input side and the output side of news recommenders.
