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home / papers / 2026 / A Computational Diachronic Analysis of Gen Z Mental Health Discourse: A Large-scale Reddit Corpus Study from Pre- to Post-COVID

A Computational Diachronic Analysis of Gen Z Mental Health Discourse: A Large-scale Reddit Corpus Study from Pre- to Post-COVID

Summary

This paper asks how Generation Z talks about mental health online and how that talk changed across the COVID-19 pandemic, a question that earlier, mostly small-scale qualitative work had not addressed at scale. Covering 2017 to 2025, the author assembled a Reddit dataset exceeding 3 million comments and posts, sourced from mental health support subreddits and Gen Z identity forums. Users who participated in both kinds of community were treated as a proxy for Gen Z, and users active only in mental health spaces formed a non-Gen Z comparison group. The data are split into pre-pandemic, pandemic and post-pandemic periods and analysed in three ways: diachronic keyness, BERTopic topic modeling and DistilBERT sentiment classification.

The results show three patterns:

  • Gen Z support exchanges follow ritual-like patterns, and strongly negative self-disclosure works as a way to signal authenticity.
  • The pandemic reframed existing topics toward physical symptoms, and sentiment kept falling after the pandemic ended.
  • Gen Z dwells on abstract, existential worries, while other users focus on practical matters.

The work offers a replicable pipeline for studying youth discourse and argues that digital interventions should respect Gen Z's ways of voicing distress rather than pathologize them.

Key points

  • Corpus size and scope: over 3 million submissions and comments from more than 320 thousand unique users, collected from 11 subreddits between 2017 and 2025.
  • Subreddits:
    • Mental health: r/depression, r/Anxiety, r/mentalhealth, r/socialanxiety, r/bipolar, r/bipolar2.
    • Gen Z identity: r/Teenagers, r/GenZ, r/teenagersnew, r/TeenagersButBetter, r/teenagersbuthot.
  • Periods compared: pre-COVID (2017–2019), COVID (2020–2022) and post-COVID (2023–2025).
  • Models and validation:
    • Topics: BERTopic with all-MiniLM-L6-v2 embeddings.
    • Sentiment: distilbert-base-uncased-finetuned-sst-2-english.
    • LDA and VADER served as cross-checks and gave similar trends, but only the Transformer results are reported.
  • Availability: the dataset is public on GitHub as FMB678/GenZ-Mental-Health-Reddit-Corpus. The paper appeared at LREC 2026, pp. 11199–11208.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Discourse analysis

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4g79vhy3fcqz

@inproceedings{mao2026computational,
  title = {{A Computational Diachronic Analysis of Gen Z Mental Health Discourse: A Large-scale Reddit Corpus Study from Pre- to Post-COVID}},
  author = {Mao, Felix},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4g79vhy3fcqz},
  url = {https://doi.org/10.63317/4g79vhy3fcqz}
}

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