Leveraging enhanced BERT models for detecting suicidal ideation in Thai social media content amidst COVID-19

Abstract

During the COVID-19 pandemic, people experienced major lifestyle changes including enforced isolation which resulted in an increase in suicidal ideation. In the face of isolation, individuals sought avenues to express themselves, and social media platforms have emerged as a primary choice. It is crucial to detect and analyze expressions of suicidal ideation and emotional distress on these platforms in order to monitor and prevent suicide. This study aims to fill the research gap on analyzing suicidal ideation and emotional distress expressed in the Thai language on social media, specifically, Twitter. We present a dataset of 2,400 manually annotated Thai tweets marked for suicidal ideation and emotions. We then designed a deep learning model using key features extracted from the tweets to predict these factors and compared its performance with other common machine learning models. Our model outperformed the baseline models, with an F1-score of 93 % for predicting suicidal ideation and an F1-score of 88 % for predicting emotions. Using this model, we analyzed 67,627 tweets from 2019 to 2020 and found a marked increase in tweets expressing suicidal thoughts and sadness, up 40.97 % and 21.28 % respectively from 2019 to 2020. The findings indicate that the pandemic had a significant impact on the mental health of the Thai population. Our study provides a tool for identifying and monitoring suicidal thinking in similar settings and offers insight into the COVID-19 impact on mental health in Thailand. The annotated dataset will serve as a valuable resource for further research in this field.