PDScore: Personality driven computational psychological model for depression score estimation using social media text

Objective Recent studies show that the personality factors such as neuroticism, extraversion, and conscientiousness are linked to depression. This paper presents a computational model, PDScore, to explore the relationship between personality traits and depression scores obtained from an analysis of social media text by monitoring individuals’ social media posts over a specified time frame. Method The study aims to understand how different personality traits influence depression levels. Using the 3-D psychological theory (VAD) proposed by Mehrabian as a foundation, initial depression scores are derived. Subsequent analysis employs fuzzy logic to account for uncertainty arising from personality variability, resulting in refined depression score estimations. Results Validation is performed in two ways: using the eRisk2018 Dataset and Expert-Supervised Validation Using Psychometric Tools. First, the results are compared with eRisk2018 datasets and compared with the existing models. The proposed model outperformed all the existing methods with 0.84 F1-score. Then, a data is collected from 57 participants with approximately 35 to 40 posts per participant, resulting in a total of over 2150 posts. we applied our method to compute corresponding depression scores and compared them with the psychologists’ assessments. It demonstrates a correlation of 0.985 between the manual score and final depression score. The comparison yielded an accuracy of 94.7% in classification and a Root Mean Square Error (RMSE) of 0.089, indicating strong alignment and reinforcing the empirical validity of the proposed model. We consider this initial study a proof-of-concept that demonstrates the feasibility and potential of integrating personality factors and linguistic features for depression scoring. Conclusions The paper delves into the methodology and findings in detail, shedding light on the complex interplay between personality and mental health in online information contexts. The results reveal that personality plays an important role in estimation of depression score.