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

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...

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Bibliographic Details
Published in:International social science journal 2023-03, Vol.73 (247), p.29-50
Main Authors: Joshi, Deepali, Patwardhan, Manasi
Format: Article
Language:English
Subjects:
Behavior
Behavior change
Classification
Clustering
Cognitive style
Mental disorders
Mental health
Prevention
Social media
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Summary: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.
ISSN:0020-8701
1468-2451
DOI:10.1111/issj.12368