Anxious Depression Prediction in Real-time Social Data
This work addresses mental health monitoring for social media users, but it is incremental as it applies existing classifiers to a new domain with a novel feature set.
The paper tackled the problem of predicting anxious depression from real-time social media data by proposing a model that uses linguistic cues and user posting patterns, achieving a classification accuracy of 85.09% on a sample of 100 users.
Mental well-being and social media have been closely related domains of study. In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed. This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector <word, timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is analyzed for irregularities and opinion polarity analytics is done to find inconsistencies in posting behaviour. The model is trained using three classifiers (multinomial naïve bayes, gradient boosting, and random forest) and majority voting using an ensemble voting classifier is done. Preliminary results are evaluated for tweets of sampled 100 users and the proposed model achieves a classification accuracy of 85.09%.