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
Facebook, Twitter, Instagram과 같은 소셜 미디어 채널은 우리 세상을 영원히 바꿔 놓았습니다. 이제 사람들은 그 어느 때보다 점점 더 연결되어 있으며 일종의 디지털 페르소나를 드러냅니다. 소셜 미디어에는 확실히 몇 가지 놀라운 기능이 있지만 단점도 부인할 수 없습니다. 최근 연구에 따르면 소셜 미디어 사이트의 높은 사용률과 우울증 증가 사이의 상관 관계가 밝혀졌습니다. 본 연구는 네트워크 행동과 트윗을 기반으로 우울증에 걸린 트위터 사용자를 탐지하기 위한 기계 학습 기술을 활용하는 것을 목표로 합니다. 이를 위해 우리는 사용자의 네트워크 활동과 트윗에서 추출한 특징을 사용하여 사용자가 우울한지 여부를 구별하기 위해 분류기를 훈련하고 테스트했습니다. 그 결과, 더 많은 특징을 사용할수록 우울한 사용자를 탐지하는 정확도와 F-measure 점수가 더 높아지는 것으로 나타났습니다. 이 방법은 우울증이나 기타 정신 질환을 조기에 발견하기 위한 데이터 기반 예측 접근 방식입니다. 본 연구의 주요 기여는 특징의 탐색 부분과 우울증 수준 탐지에 미치는 영향입니다.
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
부
@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},}
부
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 -