Incorporating historical information by disentangling hidden representations for mental health surveillance on social media

Naseem, Usman, Thapa, Surendrabikram, Zhang, Qi, Hu, Liang, Rashid, Junaid, and Nasim, Mehwish (2024) Incorporating historical information by disentangling hidden representations for mental health surveillance on social media. Social Network Analysis and Mining, 14 (1). 9.

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Abstract

The growing need to identify mental health conditions has paved the way for automated computational methods for mental health surveillance on social media. However, inferring the accurate state of a user’s mind requires understanding the history of the user’s mental health condition, which is critical for identifying the mental health landscape of at-risk users. Recent methods have offered performance improvement to address this challenging area; however, they assume that the intervals between all historical social media posts are equally important, making them unable to capture the dynamic temporal patterns of historical posts. In this work, we address this gap and incorporate a time-aware framework that jointly learns the context of the user’s historical posts and temporal posting irregularities by disentangling representations of different time intervals in the users’ historical posts and adaptively selecting the most important interval for each sample at each time step. First, the hidden state of the RNN is disentangled into multiple independently updated small hidden states to model users’ historical posting information. Then, at each time step, the temporal context information is used to modulate the features of different posts, selecting the most important interval within the historical posts. Experimental results on two mental health (i.e., depression and self-harm) Reddit datasets show that our method outperforms state-of-the-art methods for mental health surveillance on social media.

Item ID: 81989
Item Type: Article (Research - C1)
ISSN: 1869-5469
Keywords: Mental health, Recurrent neural networks, Suicide risk assessment, Time-aware
Copyright Information: © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2023.
Date Deposited: 25 Mar 2025 02:45
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 40%
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 40%
42 HEALTH SCIENCES > 4299 Other health sciences > 429999 Other health sciences not elsewhere classified @ 20%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220499 Information systems, technologies and services not elsewhere classified @ 50%
20 HEALTH > 2004 Public health (excl. specific population health) > 200499 Public health (excl. specific population health) not elsewhere classified @ 50%
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