Knowledge Graph Representation for Political Information Sources
Tinatin Osmonova Alexey Tikhonov Ivan P. Yamshchikov
With the rise of computational social science, many scholars utilize data
analysis and natural language processing tools to analyze social media, news
articles, and other accessible data sources for examining political and social
discourse. Particularly, the study of the emergence of echo-chambers due to the
dissemination of specific information has become a topic of interest in mixed
methods research areas. In this paper, we analyze data collected from two news
portals, Breitbart News (BN) and New York Times (NYT) to prove the hypothesis
that the formation of echo-chambers can be partially explained on the level of
an individual information consumption rather than a collective topology of
individuals' social networks. Our research findings are presented through
knowledge graphs, utilizing a dataset spanning 11.5 years gathered from BN and
NYT media portals. We demonstrate that the application of knowledge
representation techniques to the aforementioned news streams highlights,
contrary to common assumptions, shows relative "internal" neutrality of both
sources and polarizing attitude towards a small fraction of entities.
Additionally, we argue that such characteristics in information sources lead to
fundamental disparities in audience worldviews, potentially acting as a
catalyst for the formation of echo-chambers.