The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Les réseaux sociaux, tels que Facebook, Twitter et Instagram, ont modifié notre monde à jamais. Les gens sont désormais de plus en plus connectés et révèlent une sorte de personnalité numérique. Bien que les médias sociaux présentent certainement plusieurs caractéristiques remarquables, leurs inconvénients sont également indéniables. Des études récentes ont indiqué une corrélation entre une utilisation élevée des sites de médias sociaux et une augmentation de la dépression. La présente étude vise à exploiter les techniques d'apprentissage automatique pour détecter un utilisateur probable de Twitter déprimé en fonction de son comportement sur le réseau et de ses tweets. Pour cela, nous avons formé et testé des classificateurs pour distinguer si un utilisateur est déprimé ou non en utilisant des caractéristiques extraites de ses activités sur le réseau et ses tweets. Les résultats ont montré que plus les fonctionnalités sont utilisées, plus les scores de précision et de mesure F dans la détection des utilisateurs déprimés sont élevés. Cette méthode est une approche prédictive basée sur les données pour la détection précoce de la dépression ou d’autres maladies mentales. La principale contribution de cette étude est la partie exploration des caractéristiques et son impact sur la détection du niveau de dépression.
Hatoon S. ALSAGRI
Al Imam Mohammad Ibn Saud Islamic University (IMSIU)
Mourad YKHLEF
King Saud University
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Hatoon S. ALSAGRI, Mourad YKHLEF, "Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 8, pp. 1825-1832, August 2020, doi: 10.1587/transinf.2020EDP7023.
Abstract: Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7023/_p
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@ARTICLE{e103-d_8_1825,
author={Hatoon S. ALSAGRI, Mourad YKHLEF, },
journal={IEICE TRANSACTIONS on Information},
title={Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features},
year={2020},
volume={E103-D},
number={8},
pages={1825-1832},
abstract={Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.},
keywords={},
doi={10.1587/transinf.2020EDP7023},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features
T2 - IEICE TRANSACTIONS on Information
SP - 1825
EP - 1832
AU - Hatoon S. ALSAGRI
AU - Mourad YKHLEF
PY - 2020
DO - 10.1587/transinf.2020EDP7023
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2020
AB - Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.
ER -