@inproceedings{hassib-etal-2022-aradepsu,
title = "{A}ra{D}ep{S}u: Detecting Depression and Suicidal Ideation in {A}rabic Tweets Using Transformers",
author = "Hassib, Mariam and
Hossam, Nancy and
Sameh, Jolie and
Torki, Marwan",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.28",
doi = "10.18653/v1/2022.wanlp-1.28",
pages = "302--311",
abstract = "Among mental health diseases, depression is one of the most severe, as it often leads to suicide which is the fourth leading cause of death in the Middle East. In the Middle East, Egypt has the highest percentage of suicidal deaths; due to this, it is important to identify depression and suicidal ideation. In Arabic culture, there is a lack of awareness regarding the importance of diagnosing and living with mental health diseases. However, as noted for the last couple years people all over the world, including Arab citizens, tend to express their feelings openly on social media. Twitter is the most popular platform designed to enable the expression of emotions through short texts, pictures, or videos. This paper aims to predict depression and depression with suicidal ideation. Due to the tendency of people to treat social media as their personal diaries and share their deepest thoughts on social media platforms. Social data contain valuable information that can be used to identify user{'}s psychological states. We create AraDepSu dataset by scrapping tweets from twitter and manually labelling them. We expand the diversity of user tweets, by adding a neutral label ({``}neutral{''}) so the dataset include three classes ({``}depressed{''}, {``}suicidal{''}, {``}neutral{''}). Then we train our AraDepSu dataset on 30+ different transformer models. We find that the best-performing model is MARBERT with accuracy, precision, recall and F1-Score values of 91.20{\%}, 88.74{\%}, 88.50{\%} and 88.75{\%}.",
}
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<abstract>Among mental health diseases, depression is one of the most severe, as it often leads to suicide which is the fourth leading cause of death in the Middle East. In the Middle East, Egypt has the highest percentage of suicidal deaths; due to this, it is important to identify depression and suicidal ideation. In Arabic culture, there is a lack of awareness regarding the importance of diagnosing and living with mental health diseases. However, as noted for the last couple years people all over the world, including Arab citizens, tend to express their feelings openly on social media. Twitter is the most popular platform designed to enable the expression of emotions through short texts, pictures, or videos. This paper aims to predict depression and depression with suicidal ideation. Due to the tendency of people to treat social media as their personal diaries and share their deepest thoughts on social media platforms. Social data contain valuable information that can be used to identify user’s psychological states. We create AraDepSu dataset by scrapping tweets from twitter and manually labelling them. We expand the diversity of user tweets, by adding a neutral label (“neutral”) so the dataset include three classes (“depressed”, “suicidal”, “neutral”). Then we train our AraDepSu dataset on 30+ different transformer models. We find that the best-performing model is MARBERT with accuracy, precision, recall and F1-Score values of 91.20%, 88.74%, 88.50% and 88.75%.</abstract>
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%0 Conference Proceedings
%T AraDepSu: Detecting Depression and Suicidal Ideation in Arabic Tweets Using Transformers
%A Hassib, Mariam
%A Hossam, Nancy
%A Sameh, Jolie
%A Torki, Marwan
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F hassib-etal-2022-aradepsu
%X Among mental health diseases, depression is one of the most severe, as it often leads to suicide which is the fourth leading cause of death in the Middle East. In the Middle East, Egypt has the highest percentage of suicidal deaths; due to this, it is important to identify depression and suicidal ideation. In Arabic culture, there is a lack of awareness regarding the importance of diagnosing and living with mental health diseases. However, as noted for the last couple years people all over the world, including Arab citizens, tend to express their feelings openly on social media. Twitter is the most popular platform designed to enable the expression of emotions through short texts, pictures, or videos. This paper aims to predict depression and depression with suicidal ideation. Due to the tendency of people to treat social media as their personal diaries and share their deepest thoughts on social media platforms. Social data contain valuable information that can be used to identify user’s psychological states. We create AraDepSu dataset by scrapping tweets from twitter and manually labelling them. We expand the diversity of user tweets, by adding a neutral label (“neutral”) so the dataset include three classes (“depressed”, “suicidal”, “neutral”). Then we train our AraDepSu dataset on 30+ different transformer models. We find that the best-performing model is MARBERT with accuracy, precision, recall and F1-Score values of 91.20%, 88.74%, 88.50% and 88.75%.
%R 10.18653/v1/2022.wanlp-1.28
%U https://aclanthology.org/2022.wanlp-1.28
%U https://doi.org/10.18653/v1/2022.wanlp-1.28
%P 302-311
Markdown (Informal)
[AraDepSu: Detecting Depression and Suicidal Ideation in Arabic Tweets Using Transformers](https://aclanthology.org/2022.wanlp-1.28) (Hassib et al., WANLP 2022)
ACL