Negatively Correlated Noisy Learners for At-risk User Detection on Social Networks: A Study on Depression, Anorexia, Self-harm and Suicide - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles IEEE Transactions on Knowledge and Data Engineering Year : 2023

Negatively Correlated Noisy Learners for At-risk User Detection on Social Networks: A Study on Depression, Anorexia, Self-harm and Suicide

Abstract

Mental and physical health are strongly linked in a bidirectional relationship. Due to the stigma, ignorance, prejudice, fear, and many other reasons, there exists a large universal treatment gap for people with mental disorders. This could motivate those at-risk individuals to find their way into social networks, asking for information or emotional support. Language could provide a natural eyepiece for the study and detection of such at-risk individuals through their writings on social media platforms. In this paper, we consider the problem of detecting at-risk users with clear signs of depression, anorexia, self-harm, and suicidal thoughts. We introduce NCNL, a novel deep learning ensemble architecture that makes use of multiple noisy base learners in Negative Correlation Learning (NCL) configuration for text classification. NCNL is designed to be, backbone-independent, and we examine it with modern Transformer-based architectures. We evaluate our models on six different tasks for at-risk user detection and classification. Our models achieve significant improvements over existing state-of-the-art results reported for five out of the six tasks. Extensive experiments show how NCNL improves diversity over the classical conventional ensemble and the effect of using noisy base learners.
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Dates and versions

lirmm-03736669 , version 1 (22-07-2022)

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Waleed Ragheb, Jérôme Azé, Sandra Bringay, Maximilien Servajean. Negatively Correlated Noisy Learners for At-risk User Detection on Social Networks: A Study on Depression, Anorexia, Self-harm and Suicide. IEEE Transactions on Knowledge and Data Engineering, 2023, 35 (1), pp.770-783. ⟨10.1109/TKDE.2021.3078898⟩. ⟨lirmm-03736669⟩
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