mlsanityData to 7 Oct 2026

Detecting Depression in Thai Blog Posts: a Dataset and a Baseline

arXiv 2111.04574 ↗First posted 8 Nov 2021cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53% accuracy with a Thai BERT model in detecting depression. This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia. Our corpus, code and trained models have been released openly on Zenodo.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 5 of its 35 references that are in the map. First posted on arXiv 8 Nov 2021.

Builds on

5 of 35 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  3. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  4. 2021Endangered Languages are not Low-Resourced!cited by 14
  5. 2021Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baselinecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 3; 2026: 2.

6 papers.

  1. 2025A Survey on Multilingual Mental Disorders Detection from Social Media Datacited by 3
  2. 2025SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?cited by 2
  3. 2025CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysiscited by 1
  4. 2026Large Language Models for Mental Health: A Multilingual Evaluationcited by 1
  5. 2022Harnessing Multilingual Resources to Question Answering in Arabiccited by 0
  6. 2026Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasetscited by 0