{"title":"在社交媒体上检测抑郁迹象:一种机器学习分析和评估","authors":"Phi Ta , Nha Tran , Hung Nguyen , Hien D. Nguyen","doi":"10.1016/j.sftr.2025.100827","DOIUrl":null,"url":null,"abstract":"<div><div>Depression has become a growing concern due to its detrimental effects on both personal functioning and interpersonal relationships. In contemporary society, it is of utmost urgency to research and develop systems capable of detecting symptoms of depression on social media. Our study is not merely a survey, but a comprehensive investigation aimed at uncovering valuable insights and trends in the detection of depression on social media platforms. Our findings present a consolidated map of current methodologies, highlight key trends, and, through experimental results, provide clear performance benchmarks for both post-level and user-level techniques. By integrating insights from both the extensive literature review and practical experiments, this work clarifies existing challenges, establishes performance baselines, and proposes empirically grounded future directions to advance the development of more effective and reliable depression detection systems on social networks. This work opens a promising future for addressing the challenge of detecting depression on social media and contributes to enhancing the effectiveness of depression detection systems, ultimately aiding individuals affected by the adverse effects of depression.</div></div>","PeriodicalId":34478,"journal":{"name":"Sustainable Futures","volume":"10 ","pages":"Article 100827"},"PeriodicalIF":3.3000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting signs of depression on social media: A machine learning analysis and evaluation\",\"authors\":\"Phi Ta , Nha Tran , Hung Nguyen , Hien D. Nguyen\",\"doi\":\"10.1016/j.sftr.2025.100827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Depression has become a growing concern due to its detrimental effects on both personal functioning and interpersonal relationships. In contemporary society, it is of utmost urgency to research and develop systems capable of detecting symptoms of depression on social media. Our study is not merely a survey, but a comprehensive investigation aimed at uncovering valuable insights and trends in the detection of depression on social media platforms. Our findings present a consolidated map of current methodologies, highlight key trends, and, through experimental results, provide clear performance benchmarks for both post-level and user-level techniques. By integrating insights from both the extensive literature review and practical experiments, this work clarifies existing challenges, establishes performance baselines, and proposes empirically grounded future directions to advance the development of more effective and reliable depression detection systems on social networks. This work opens a promising future for addressing the challenge of detecting depression on social media and contributes to enhancing the effectiveness of depression detection systems, ultimately aiding individuals affected by the adverse effects of depression.</div></div>\",\"PeriodicalId\":34478,\"journal\":{\"name\":\"Sustainable Futures\",\"volume\":\"10 \",\"pages\":\"Article 100827\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Futures\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666188825003922\",\"RegionNum\":2,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Futures","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666188825003922","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Detecting signs of depression on social media: A machine learning analysis and evaluation
Depression has become a growing concern due to its detrimental effects on both personal functioning and interpersonal relationships. In contemporary society, it is of utmost urgency to research and develop systems capable of detecting symptoms of depression on social media. Our study is not merely a survey, but a comprehensive investigation aimed at uncovering valuable insights and trends in the detection of depression on social media platforms. Our findings present a consolidated map of current methodologies, highlight key trends, and, through experimental results, provide clear performance benchmarks for both post-level and user-level techniques. By integrating insights from both the extensive literature review and practical experiments, this work clarifies existing challenges, establishes performance baselines, and proposes empirically grounded future directions to advance the development of more effective and reliable depression detection systems on social networks. This work opens a promising future for addressing the challenge of detecting depression on social media and contributes to enhancing the effectiveness of depression detection systems, ultimately aiding individuals affected by the adverse effects of depression.
期刊介绍:
Sustainable Futures: is a journal focused on the intersection of sustainability, environment and technology from various disciplines in social sciences, and their larger implications for corporation, government, education institutions, regions and society both at present and in the future. It provides an advanced platform for studies related to sustainability and sustainable development in society, economics, environment, and culture. The scope of the journal is broad and encourages interdisciplinary research, as well as welcoming theoretical and practical research from all methodological approaches.