Analysis of depression in social media texts through the Patient Health Questionnaire-9 and natural language processing

Abstract

Objective Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users’ depression based on their results. Methods First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whether a user’s sentence is related to depression, and the 0–9 sentence classifier, which further categorizes the user sentence based on the depression symptomology of the Patient Health Questionnaire-9. Then the depression classifier, which is a logistic regression model, was generated to classify the sentence writer’s depression. These trained sentence classifiers and the depression classifier were used to analyze the social media textual data of users and establish their depression. Results Our experimental results showed that the proposed depression classifier showed 68.3% average accuracy, which was better than the baseline depression classifier that used only the Y/N sentence classifier and had 53.3% average accuracy. Conclusions This study is significant in that it demonstrates the possibility of determining depression from only social media users’ textual data.

A comprehensive review on emerging trends in the dynamic evolution of digital addiction and depression

“…The main theme of Social-Media-Addiction (0.93, 0.5) was determined to have a strong relationship with Self-Regulation, Social-Isolation, Deep-Learning, Emerging-Adults, Postpartum-Depression, Race/Ethnicity, Racial-Discrimination, and Schizophrenia themes. These studies on Self-Regulation (Akıl et al, 2022), Social-Isolation (Sewall et al, 2022), Deep-Learning (Kim et al, 2022), Emerging-Adults (Cano et al, 2021), Postpartum-Depression (Shatte et al, 2020), Race/Ethnicity (Nereim et al, 2022), Racial-Discrimination (Pan et al, 2021), and Schizophrenia (Li et al, 2020) could be listed to support the emergent sub-themes in the Social-Media-Addiction cluster network. The main theme of Internet-Addiction (0.86, 0.93) was found to be related to Students, Teachers, Cyberbullying-Victimization, Disordered-Sleep, Family-Factors, Impulsivity, K-12-Education, and Self-Efficacy themes.…”

Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT

“…For instance, Kabir et al [18] employ BERT and DistilBERT models to classify depression and its severity in four categories (non-depressed, mild, moderate, and severe) using tweets. Kim et al [19] used two separate BERT-based classifiers to detect users' depression based on social media texts. In another study, Ji et al [20] customize BERT and RoBERTa models for the mental health care domain through training them on mental-health-related subreddits, including 'r/depression', 'r/SuicideWatch', 'r/Anxiety', 'r/offmychest', 'r/bipolar', 'r/mentalillness', and 'r/mentalhealth', resulting in improved performance in mental health detection tasks.…”

Comprehensive Review and Proposed Model for Multimodal Depression Detection

“…SVM-RFE outperformed the statistical filter as a feature selection method, but the differences in performance measures between the two methods were not substantial. Kim NH et al's model [5] used natural language processing to categorize phrases made by social media users according to the nine symptoms of depression included in the Patient Health Questionnaire-9 (PHQ-9). The model's output was used to determine the user's level of depression.…”