The rapid expansion of social media platforms has created enormous amounts of user-created content and behavioral information, providing the computational means to study human personality and psychology. This study creates a temporal deep learning model and Gated Recurrent Units (GRUs) to predict personality traits with behavioral and content-based features obtained from Facebook social media. The research fits the Big Five Personality Traits paradigm and aims at modelling temporal relationships in user activity patterns, including the frequency of posts, linguistic behavior, and social interaction relationships, to identify latent psychological aspects. A GRU-based framework was created to model sequential dependencies and contextual relationships among the activity timelines of the user. To evaluate the model performance and reliability, two comparison baselines: Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN were run within the same experimental conditions. Model evaluation also used regression (Mean Absolute Error, MAE; Coefficient of Determination, R2) and classification (Accuracy, Precision, Recall, F1-score, and AUC-ROC) metrics, which were also validated using a 10-fold cross-validation process to ensure that they were stable and generalizable. The experimental findings indicated that the proposed GRU model was always better in all the evaluation metrics compared to the base models. It had the least MAE (0.00825) and the highest R2 (0.9917) and showed outstanding predictive reliability. GRU had a high accuracy (96.8) and F1-score (0.96) and AUC-ROC (0.98), which were better than LSTM (F1 = 0.95) and ANN (F1 = 0.84), in classification performance. The analysis at the trait level showed that the predictive accuracy is high on all dimensions of personality, with Agreeableness (R2 =0.9942, F1 =0.97) being the most accurately predicted and Extraversion (R2 =0.9862) having a high predictive consistency. The findings of the cross-validation also confirmed the strength and the external validity of the GRU framework.
Abid et al. (Thu,) studied this question.