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

arXiv: https://arxiv.org/abs/2111.04574 · HTML: https://mlsanity.com/paper/2111.04574/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Endangered Languages are not Low-Resourced!](https://mlsanity.com/paper/2103.09567/) (2021)
- [Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline](https://mlsanity.com/paper/2106.03389/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)
- [CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis](https://mlsanity.com/paper/2510.11233/) (2025)
- [Large Language Models for Mental Health: A Multilingual Evaluation](https://mlsanity.com/paper/2602.02440/) (2026)
- [Harnessing Multilingual Resources to Question Answering in Arabic](https://mlsanity.com/paper/2205.08024/) (2022)
- [Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets](https://mlsanity.com/paper/2607.03540/) (2026)

## 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.