Computational text analysis reveals shifting literary themes in online reading communities, indicating that global crises intensify and politicize community reading habits.
Online communities increasingly function as informal collective memory archives, preserving and curating literary discussions outside traditional digital libraries. This paper investigates the evolution of book-related discourse within the Reddit community r/books across three periods surrounding the COVID-19 pandemic: pre-pandemic (2019), pandemic (2020–2021), and later period (2022). Leveraging a combination of text mining, named entity recognition, and keyword extraction, we examine changes in book mentions, co-occurrence patterns, dominant genres, and thematic emphases in user comments. Our findings indicate that discussions during the pandemic period largely intensified and diversified existing reading interests rather than introducing entirely new preferences. We observe temporal shifts in discussion topics, from narrative-focused critiques to broader social and political themes during the pandemic years, followed by increased attention to introspective and experimental topics in the later period. While these patterns coincide with the pandemic period, they should be interpreted alongside other social, political, and cultural developments that occurred during the same period and may also have influenced literary discussions. Overall, the study highlights the value of computational analysis of community-generated digital archives for documenting evolving literary practices and understanding how online reading communities reflect broader historical contexts.
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Azizifard et al. (2026) studied this question.
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