Evaluating the Efficacy of Base Models for Technostress Detection

The widespread use of technology has led to an increase in technostress which is a phenomenon where individuals experience stress and anxiety due to their interactions with technology. As social media platforms become increasingly integral to daily life, detecting technostress from online interactions has become a pressing concern and an avenue to enrich the research in the area of detecting technostress. This study evaluates the performance of selected base models on X (Twitter data). Also, the study investigated the effectiveness of a feature extraction technique for the improvement of the model performance through data preprocessing. The study made use of the dataset of X posts (Sentiment140) obtained from the Standford University. The extracted features were used to train and evaluate four base models: Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), and Light Gradient Boosting Machine (LGBM). The performance of each model was evaluated based on accuracy, precision, recall, F1-score and Kappa statistics. The RF model outperformed other base models with accuracy, precision, recall, f1-score, and Kappa score values of 88.03%, 85.98%, 85.68%, 85.79% and 79.81% respectively. The results highlight the importance of preprocessing and feature extraction techniques in improving model performance; contributes to the development of more effective technostress detection systems and provide insights into the application of machine learning algorithms for analysing online interactions.