Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks
Issued Date
2022-09-01
Resource Type
ISSN
15320464
Scopus ID
2-s2.0-85135712053
Pubmed ID
35908625
Journal Title
Journal of Biomedical Informatics
Volume
133
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Biomedical Informatics Vol.133 (2022)
Suggested Citation
Noraset T., Chatrinan K., Tawichsri T., Thaipisutikul T., Tuarob S. Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks. Journal of Biomedical Informatics Vol.133 (2022). doi:10.1016/j.jbi.2022.104145 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84262
Title
Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks
Author's Affiliation
Other Contributor(s)
Abstract
In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. However, because of the limits of fundamental natural language processing tools and labeled corpora in countries with limited natural language resources, such as Thailand, implementing social media systems to monitor mental health signals could be challenging. This paper presents LAPoMM, a novel framework for monitoring real-time mental health indicators from social media data without using labeled datasets in low-resource languages. Specifically, we use cross-lingual methods to train language-agnostic models and validate our framework by examining cross-correlations between the aggregate predicted mental signals and real-world administrative data from Thailand's Department of Mental Health, which includes monthly depression patients and reported cases of suicidal attempts. A combination of a language-agnostic representation and a deep learning classification model outperforms all other cross-lingual techniques for recognizing various mental signals in tweets, such as emotions, sentiments, and suicidal tendencies. The correlation analyses discover a strong positive relationship between actual depression cases and the predicted negative sentiment signals as well as suicide attempts and negative signals (e.g., fear, sadness, and disgust) and suicidal tendency. These findings establish the effectiveness of our proposed framework and its potential applications in monitoring population-level mental health using large-scale social media data. Furthermore, because the language-agnostic model utilized in the methodology is capable of supporting a wide range of languages, the proposed LAPoMM framework can be easily generalized for analogous applications in other countries with limited language resources.