An ABC approach for depression signs on social networks posts

Amina Madani, Fatima Boumahdi, Anfel Boukenaoui, Mohamed Chaouki Kritli, Asma Ghribi, Fatma Limani and Hamza Hentabli

International Journal of Data Mining, Modelling and Management, 2023, vol. 15, issue 3, 275-296

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. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=132972 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:15:y:2023:i:3:p:275-296

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().