Tracing prodromal behaviour by analysing data patterns from social media with ensemble machine learning approach

Abstract

This paper presents a novel solution for tracing prodromal behaviour of Twitter users for early detection and prevention of mental illness. A very large number of people are using Twitter to share their daily happenings. This is creating a rich source of data portraying individual behaviour. Machine learning can be used to screen individual users who show prodromal behavioural change over a period and need to consult a psychiatrist. The system comprises of an ensemble model with multiple modules including classification and clustering in addition to lexicon‐based approach, and, for the final verdict, a voting system which emphasises a better precision model is proposed. The model has been validated and tested on standard Self‐Reported Mental Health Diagnoses dataset and promises to give a better result than the baseline.

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