Reddit in the Time of COVID
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Abstract (arXiv)
When the COVID-19 pandemic hit, much of life moved online. Platforms of all types reported surges of activity, and people remarked on the various important functions that online platforms suddenly fulfilled. However, researchers lack a rigorous understanding of the pandemic's impacts on social platforms, and whether they were temporary or long-lasting. We present a conceptual framework for studying the large-scale evolution of social platforms and apply it to the study of Reddit's history, with a particular focus on the COVID-19 pandemic. We study platform evolution through two key dimensions: structure vs. content and macro- vs. micro-level analysis. Structural signals help us quantify how much behavior changed, while content analysis clarifies exactly how it changed. Applying these at the macro-level illuminates platform-wide changes, while at the micro-level we study impacts on individual users. We illustrate the value of this approach by showing the extraordinary and ordinary changes Reddit went through during the pandemic. First, we show that typically when rapid growth occurs, it is driven by a few concentrated communities and within a narrow slice of language use. However, Reddit's growth throughout COVID-19 was spread across disparate communities and languages. Second, all groups were equally affected in their change of interest, but veteran users tended to invoke COVID-related language more than newer users. Third, the new wave of users that arrived following COVID-19 was fundamentally different from previous cohorts of new users in terms of interests, activity, and likelihood of staying active on the platform. These findings provide a more rigorous understanding of how an online platform changed during the global pandemic.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Apr 2023.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Indexcited by 1
- 2026Large Language Models Unpack Complex Political Opinions through Target-Stance Extractioncited by 1
- 2025Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Frameworkcited by 0
- 2026Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction7 Mar 2026
- 2025Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework14 Mar 2025
- 2024Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index22 Dec 2024