János Sebestyén Pap (LinkedIn, E-mail)
This thesis examines political polarization in Hungarian online news media through patterns of mentions of individuals’ names. The research is based on the hypothesis that polarization manifests not only in opinions but also in which public figures the media highlight over others, and what structural patterns and groupings can be observed among them. The empirical analysis is based on a text corpus comprising articles published between 1998 and 2022. The study combines natural language processing and network analysis methods, using named entity recognition to identify individuals and then constructing news site–person type-pair networks. Relationships are filtered using the comparative advantage index and evaluated using a null-model-based approach with the help of a pairwise configuration model. The study examines the differentiation of news sites based on co-occurrence within and between groups, as well as a polarization index calculated from these data. The results indicate significant polarization following 2017, which is consistent with previous research on the political fragmentation of the Hungarian media system. This study contributes to the network-based measurement of media polarization; however, its limitations include the accuracy of entity recognition and the representativeness of the data.