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Abstract The rise of homogenization and polarization in the news may inhibit individuals’ understanding of an issue and the functioning of a democratic society. This study applies a network approach to understanding patterns of semantic similarity and divergence across news coverage. Specifically, we focus on how (a) inter-organizational networks based on media ideology, (b) inter-organizational networks based on news truthfulness, and (c) public engagement that news articles received on social media may affect semantic similarity in the news. We use large-scale user logs data on social media platforms (i.e., Facebook and Twitter) and news text data from more than 100 news organizations over 10 months to examine the three potential processes. Our results show that the similarity between news organizations in terms of media ideology and news truthfulness is positively associated with semantic similarity, whereas the public engagement that news articles received on social media is negatively associated with semantic similarity. Our study contributes to theory development in mass communication by shifting to a network paradigm that connects news organizations, news content, and news audiences. We demonstrate how scholars across communication disciplines may collaborate to integrate distinct theories, connect multiple levels, and link otherwise separate dimensions. Methodologically, we demonstrate how synchronizing network science with natural language processing and combining social media log data with text data can help to answer research questions that communication scholars are interested in. The findings’ implications for news polarization are discussed.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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