Title: An ABC approach for depression signs on social networks posts

Authors: Amina Madani; Fatima Boumahdi; Anfel Boukenaoui; Mohamed Chaouki Kritli; Asma Ghribi; Fatma Limani; Hamza Hentabli

Addresses: LRDSI Laboratory, Department of Computing, Blida1 University, Algeria ' LRDSI Laboratory, Department of Computing, Blida1 University, Algeria ' LRDSI Laboratory, Department of Computing, Blida1 University, Algeria ' LRDSI Laboratory, Department of Computing, Blida1 University, Algeria ' LRDSI Laboratory, Department of Computing, Blida1 University, Algeria ' LRDSI Laboratory, Department of Computing, Blida1 University, Algeria ' Laboratory of Advanced Electronic Systems (LSEA), Faculty of Science, University of Medea, Algeria

Abstract: Mental health is considered as one of today's world's most prominent plagues. In this paper, we aim to solve one of mental health's biggest issues, which is depression. Using the potential of social media platforms, our ABC approach is based on a combination of different deep learning models that are autoencoder, BiLSTM and CNN. We test our approach and discuss our experiments on three datasets of Reddit posts provided by 2019, 2020 and 2021 Conference and Labs of the Evaluation Forum (CLEF).

Keywords: depression signs; social networks; deep learning; convolutional neural network; CNN; BiLSTM; autoencoder.

DOI: 10.1504/IJDMMM.2023.132972

International Journal of Data Mining, Modelling and Management, 2023 Vol.15 No.3, pp.275 - 296

Received: 18 Jul 2022
Accepted: 02 Oct 2022
Published online: 22 Aug 2023 *

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