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Social media has become a ubiquitous part of the lives of many people, and provides a channel for ordinary people to voice and to hear political opinions. However, many believe that the rise of social media has lead to an increasing polarization of political views, with political bias becoming intertwined with seemingly non-political interests and behavior. In this paper, we aim to use natural language processing techniques to analyze the political bias of online social groups and the degree to which this bias correlates with non-political topics. Whereas this phenomenon has been studied extensively on networks such as Twitter, the popular social media domain Reddit has been relatively unexplored despite the structure of the platform allowing users to easily create their own interest-based subcommunities, thus providing an important and unique data source relevant to the topic at hand. This paper analyzes comment data from approximately 3,300 message boards on Reddit, with the goal of providing novel empirical knowledge about how group topic informs political bias in Reddit sub-communities. Topics of Reddit subcommunities are determined using a Latent Dirichlet Allocation model, and political bias of subcommunities is measured through direct comparison with external corpora of politically biased vocabulary; results are discussed within. Furthermore, to test the politicization of topics, we train a classifier on topic features to obtain a high accuracy of 85.2% (compared to a random-guessing baseline of 64.8%), suggesting fairly strong correlations between group topic and politically biased language in communities.
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Stored only when both papers are in the corpus — 10 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.