About: Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Creator	Zogan, Hamad
source	ArXiv
abstract	Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts). The CNN model then helps learn useful features. Our experiments show that our model outperforms several popular and strong baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.
has issue date	2020-07-03(xsd:dateTime)
has license	arxiv
sha1sum (hex)	0a21f45c4e657a46dfc5add4088b5fa561755915
resource representing a document's title	Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
resource representing a document's body	covid:0a21f45c4e657a46dfc5add4088b5fa561755915#body_text
is schema:about of	named entity 'multi-modal' named entity 'multi-modal' named entity 'posts' named entity 'features' named entity 'posting' »more»

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