id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-121200-2qys8j4u	Zogan, Hamad	Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media	2020-07-03		.txt	text/plain	10036	521	51	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. To be specific, this work aims to develop a new novel deep learning-based solution for improving depression detection by utilizing multi-modal features from diverse behaviour of the depressed user in social media. To this end, we propose a hybrid model comprising Bidirectional Gated Recurrent Unit (BiGRU) and Conventional Neural network (CNN) model to boost the classification of depressed users using multi-modal features and word embedding features. The most closely related recent work to ours is [23] where the authors propose a CNN-based deep learning model to classify Twitter users based on depression using multi-modal features.	./cache/cord-121200-2qys8j4u.txt	./txt/cord-121200-2qys8j4u.txt