Anxious Depression Prediction in Real-time Social Data

TL;DR

This paper introduces a real-time social media-based model for predicting anxious depression using linguistic cues, posting patterns, and ensemble classifiers, achieving 85.09% accuracy on sample Twitter data.

Contribution

It presents a novel multi-feature model combining linguistic and behavioral data for anxious depression detection in social media, with ensemble classification.

Findings

Achieved 85.09% accuracy in predicting anxious depression.

Utilized a 5-tuple feature set including word, timing, frequency, sentiment, and contrast.

Demonstrated effectiveness of ensemble classifiers in mental health prediction.

Abstract

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 na\"ive 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%.