@inproceedings{graff-krishnaswamy-2026-identifying,
title = "Identifying Contexts of Distress in College Students' {R}eddit Posts: A Comparative Study of Classical {NLP} and Large Language Models",
author = "Graff, Carine and
Krishnaswamy, Nikhil",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.758/",
doi = "10.63317/2k99z869ni4v",
pages = "9657--9668",
abstract = "Mental health is a salient and growing societal concern among college students. Social media platforms such as Reddit offer a rich source of data regarding how students talk about their mental health, and NLP tools may potentially assist in identifying when a student is struggling. In this paper, we investigate how different NLP tools can be used to extract context surrounding college students expressions of distress. We construct a novel dataset from Reddit posts (College Distress on Reddit, or CDR), and examine the ``classical NLP pipeline'', and modern generative LLMs on this data. Our dataset exploration is conducted in parallel with, and contrasted against the Dreaddit dataset to examine cross-domain variation. Results show that standard or ``classical'' NLP tools extract a limited number of concrete entities, whereas generative models can infer more nuanced causes. However, LLMs struggle with knowledge extraction in specific content areas. Our work shows how important it is to be wary of LLMs, especially in mental health contexts."
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<abstract>Mental health is a salient and growing societal concern among college students. Social media platforms such as Reddit offer a rich source of data regarding how students talk about their mental health, and NLP tools may potentially assist in identifying when a student is struggling. In this paper, we investigate how different NLP tools can be used to extract context surrounding college students expressions of distress. We construct a novel dataset from Reddit posts (College Distress on Reddit, or CDR), and examine the “classical NLP pipeline”, and modern generative LLMs on this data. Our dataset exploration is conducted in parallel with, and contrasted against the Dreaddit dataset to examine cross-domain variation. Results show that standard or “classical” NLP tools extract a limited number of concrete entities, whereas generative models can infer more nuanced causes. However, LLMs struggle with knowledge extraction in specific content areas. Our work shows how important it is to be wary of LLMs, especially in mental health contexts.</abstract>
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%0 Conference Proceedings
%T Identifying Contexts of Distress in College Students’ Reddit Posts: A Comparative Study of Classical NLP and Large Language Models
%A Graff, Carine
%A Krishnaswamy, Nikhil
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F graff-krishnaswamy-2026-identifying
%X Mental health is a salient and growing societal concern among college students. Social media platforms such as Reddit offer a rich source of data regarding how students talk about their mental health, and NLP tools may potentially assist in identifying when a student is struggling. In this paper, we investigate how different NLP tools can be used to extract context surrounding college students expressions of distress. We construct a novel dataset from Reddit posts (College Distress on Reddit, or CDR), and examine the “classical NLP pipeline”, and modern generative LLMs on this data. Our dataset exploration is conducted in parallel with, and contrasted against the Dreaddit dataset to examine cross-domain variation. Results show that standard or “classical” NLP tools extract a limited number of concrete entities, whereas generative models can infer more nuanced causes. However, LLMs struggle with knowledge extraction in specific content areas. Our work shows how important it is to be wary of LLMs, especially in mental health contexts.
%R 10.63317/2k99z869ni4v
%U https://aclanthology.org/2026.lrec-1.758/
%U https://doi.org/10.63317/2k99z869ni4v
%P 9657-9668
Markdown (Informal)
[Identifying Contexts of Distress in College Students’ Reddit Posts: A Comparative Study of Classical NLP and Large Language Models](https://aclanthology.org/2026.lrec-1.758/) (Graff & Krishnaswamy, LREC 2026)
ACL