The anatomy of Reddit: An overview of academic research
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Abstract (arXiv)
Online forums provide rich environments where users may post questions and comments about different topics. Understanding how people behave in online forums may shed light on the fundamental mechanisms by which collective thinking emerges in a group of individuals, but it has also important practical applications, for instance to improve user experience, increase engagement or automatically identify bullying. Importantly, the datasets generated by the activity of the users are often openly available for researchers, in contrast to other sources of data in computational social science. In this survey, we map the main research directions that arose in recent years and focus primarily on the most popular platform, Reddit. We distinguish and categorise research depending on their focus on the posts or on the users, and point to different types of methodologies to extract information from the structure and dynamics of the system. We emphasize the diversity and richness of the research in terms of questions and methods, and suggest future avenues of research.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Oct 2018.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 2; 2025: 2; 2026: 2.
12 papers.
- 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 148
- 2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correctioncited by 45
- 2026Collective Behavior of AI Agents: the Case of Moltbookcited by 13
- 2024Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industrycited by 6
- 2023With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaignscited by 2
- 2024Structure and dynamics of growing networks of Reddit threadscited by 2
- 2026Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmarkcited by 2
- 2022This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaigncited by 1
- 2022Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Frameworkcited by 1
- 2025LLMs are Introvertcited by 1
- 2021The Healthy States of America: Creating a Health Taxonomy with Social Mediacited by 0
- 2025The Influence of Text Variation on User Engagement in Cross-Platform Content Sharingcited by 0
- 2026Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark18 Feb 2026
- 2026Collective Behavior of AI Agents: the Case of Moltbook9 Feb 2026
- 2025LLMs are Introvert8 Jul 2025
- 2025The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing26 Apr 2025
- 2024Structure and dynamics of growing networks of Reddit threads6 Sep 2024
- 2024Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry27 Apr 2024
- 2023With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns18 Jul 2023
- 2023Co-Writing with Opinionated Language Models Affects Users' Views1 Feb 2023
- 2022Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework9 Oct 2022
- 2022This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign14 Jan 2022
- 2021The Healthy States of America: Creating a Health Taxonomy with Social Media1 Mar 2021
- 2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction18 Feb 2021