Published on 10.07.17 in Vol 19, No 7 (2017): July
Works citing "Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study"
According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.7276):
(note that this is only a small subset of citations)
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Chancellor S, Baumer EPS, De Choudhury M. Who is the "Human" in Human-Centered Machine Learning. Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1
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Wang Z, Yu G, Tian X. Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community. International Journal of Environmental Research and Public Health 2018;16(1):54
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Ghani NA, Hamid S, Targio Hashem IA, Ahmed E. Social media big data analytics: A survey. Computers in Human Behavior 2019;101:417
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Liang Y, Zheng X, Zeng DD. A survey on big data-driven digital phenotyping of mental health. Information Fusion 2019;52:290
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Burke TA, Ammerman BA, Jacobucci R. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of Affective Disorders 2019;245:869
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Shatte AB, Hutchinson DM, Fuller-Tyszkiewicz M, Teague SJ. Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach. Cyberpsychology, Behavior, and Social Networking 2020;23(9):611
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LI L, WANG Z, ZHANG Q, WEN H. Effect of anger, anxiety, and sadness on the propagation scale of social media posts after natural disasters. Information Processing & Management 2020;57(6):102313
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Zheng Z, Yang Q, Liu Z, Qiu J, Gu J, Hao Y, Song C, Jia Z, Hao C. Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data. Journal of Medical Internet Research 2020;22(1):e13201
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Motti VG, Kalantari N, Neris V. Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America. Personal and Ubiquitous Computing 2021;25(2):321
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Oyebode O, Alqahtani F, Orji R. Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews. IEEE Access 2020;8:111141
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Yang T, Xie J, Li G, Mou N, Chen C, Zhao J, Liu Z, Lin Z. Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm. ISPRS International Journal of Geo-Information 2020;9(2):136
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Day J, Freiberg K, Hayes A, Homel R. Towards Scalable, Integrative Assessment of Children’s Self-Regulatory Capabilities: New Applications of Digital Technology. Clinical Child and Family Psychology Review 2019;22(1):90
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Chan M, Li TMH, Law YW, Wong PWC, Chau M, Cheng C, Fu KW, Bacon-Shone J, Cheng QE, Yip PSF, van Amelsvoort T. Engagement of vulnerable youths using internet platforms. PLOS ONE 2017;12(12):e0189023
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O’Connor RC, Portzky G. Looking to the Future: A Synthesis of New Developments and Challenges in Suicide Research and Prevention. Frontiers in Psychology 2018;9
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Chen L, Hu N, Shu C, Chen X. Adult attachment and self-disclosure on social networking site: A content analysis of Sina Weibo. Personality and Individual Differences 2019;138:96
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Van den Nest M, Till B, Niederkrotenthaler T. Comparing Indicators of Suicidality Among Users in Different Types of Nonprofessional Suicide Message Boards. Crisis 2019;40(2):125
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Liu D, Fu Q, Wan C, Liu X, Jiang T, Liao G, Qiu X, Liu R. Suicidal Ideation Cause Extraction From Social Texts. IEEE Access 2020;8:169333
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Asongu S, Nwachukwu J, Orim S, Pyke C. Crime and social media. Information Technology & People 2019;32(5):1215
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Lee K, Lee D, Hong HJ. Text mining analysis of teachers’ reports on student suicide in South Korea. European Child & Adolescent Psychiatry 2020;29(4):453
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. Mapping the rise of digital mental health technologies: Emerging issues for law and society. International Journal of Law and Psychiatry 2019;67:101498
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Liu LL, Li TM, Teo AR, Kato TA, Wong PW. Harnessing Social Media to Explore Youth Social Withdrawal in Three Major Cities in China: Cross-Sectional Web Survey. JMIR Mental Health 2018;5(2):e34
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Liang Y, Guo B, Yu Z, Zheng X, Wang Z, Tang L. A multi-view attention-based deep learning system for online deviant content detection. World Wide Web 2021;24(1):205
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LUO F, JIANG L, TIAN X, XIAO M, MA Y, ZHANG S. Shyness prediction and language style model construction of elementary school students. Acta Psychologica Sinica 2021;53(2):155
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Laacke S, Mueller R, Schomerus G, Salloch S. Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy. The American Journal of Bioethics 2021;21(7):4
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Liang Y, Li H, Guo B, Yu Z, Zheng X, Samtani S, Zeng DD. Fusion of heterogeneous attention mechanisms in multi-view convolutional neural network for text classification. Information Sciences 2021;548:295
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Cox CR, Moscardini EH, Cohen AS, Tucker RP. Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches. Clinical Psychology Review 2020;82:101940
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Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1
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Bauer BW, Law KC, Rogers ML, Capron DW, Bryan CJ. Editorial overview: Analytic and methodological innovations for suicide‐focused research. Suicide and Life-Threatening Behavior 2021;51(1):5
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Jacobucci R, Ammerman BA, Tyler Wilcox K. The use of text‐based responses to improve our understanding and prediction of suicide risk. Suicide and Life-Threatening Behavior 2021;51(1):55
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Cheng Q, Lui CSM. Applying text mining methods to suicide research. Suicide and Life-Threatening Behavior 2021;51(1):137
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Mansourian M, Khademi S, Marateb HR. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics 2021;11(3):393
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Rassy J, Bardon C, Dargis L, Côté L, Corthésy-Blondin L, Mörch C, Labelle R. Information and Communication Technology Use in Suicide Prevention: Scoping Review. Journal of Medical Internet Research 2021;23(5):e25288
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Kim J, Lee D, Park E. Machine Learning for Mental Health in Social Media: Bibliometric Study. Journal of Medical Internet Research 2021;23(3):e24870
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HUANG G, ZHOU X. The linguistic patterns of depressed patients. Advances in Psychological Science 2021;29(5):838
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Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668
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Liu X, Liu X. Online Suicide Identification in the Framework of Rhetorical Structure Theory (RST). Healthcare 2021;9(7):847
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Jung W, Kim D, Nam S, Zhu Y. Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning. Archives of Suicide Research 2023;27(1):13
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Feldhege J, Wolf M, Moessner M, Bauer S. Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic. European Child & Adolescent Psychiatry 2023;32(6):975
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Yip P, Xiao Y, Xu Y, Chan E, Cheung F, Chan CS, Pirkis J. Social Media Sentiments on Suicides at the New York City Landmark, Vessel: A Twitter Study. International Journal of Environmental Research and Public Health 2022;19(18):11694
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Chen Y, Liu C, Du Y, Zhang J, Yu J, Xu H. Machine learning classification model using Weibo users' social appearance anxiety. Personality and Individual Differences 2022;188:111449
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Chen X, Mo Q, Yu B, Bai X, Jia C, Zhou L, Ma Z. Hierarchical and nested associations of suicide with marriage, social support, quality of life, and depression among the elderly in rural China: Machine learning of psychological autopsy data. Frontiers in Psychiatry 2022;13
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Homan S, Gabi M, Klee N, Bachmann S, Moser A, Duri' M, Michel S, Bertram A, Maatz A, Seiler G, Stark E, Kleim B. Linguistic features of suicidal thoughts and behaviors: A systematic review. Clinical Psychology Review 2022;95:102161
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Spilsbury JC, Hernandez E, Kiley K, Gillerlane Hinkes E, Prasanna S, Shafiabadi N, Rao P, Sahoo SS. Social Service Workers’ Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform. Journal of Social Service Research 2022;48(6):739
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Kelley SW, Mhaonaigh CN, Burke L, Whelan R, Gillan CM. Machine learning of language use on Twitter reveals weak and non-specific predictions. npj Digital Medicine 2022;5(1)
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Pyenson B, Alston M, Gomberg J, Han F, Khandelwal N, Dei M, Son M, Vora J. Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency. Journal of Health Economics and Outcomes Research 2019;:32
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Yang BX, Chen P, Li XY, Yang F, Huang Z, Fu G, Luo D, Wang XQ, Li W, Wen L, Zhu J, Liu Q. Characteristics of High Suicide Risk Messages From Users of a Social Network—Sina Weibo “Tree Hole”. Frontiers in Psychiatry 2022;13
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Zhang T, Schoene AM, Ji S, Ananiadou S. Natural language processing applied to mental illness detection: a narrative review. npj Digital Medicine 2022;5(1)
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Kmetty Z, Bozsonyi K. Identifying Depression-Related Behavior on Facebook—An Experimental Study. Social Sciences 2022;11(3):135
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Kruzan KP, Bazarova NN, Whitlock J. Investigating Self-injury Support Solicitations and Responses on a Mobile Peer Support Application. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW2):1
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García-Martínez C, Oliván-Blázquez B, Fabra J, Martínez-Martínez AB, Pérez-Yus MC, López-Del-Hoyo Y. Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study. JMIR Public Health and Surveillance 2022;8(5):e31800
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Chadha A, Kaushik B. A Hybrid Deep Learning Model Using Grid Search and Cross-Validation for Effective Classification and Prediction of Suicidal Ideation from Social Network Data. New Generation Computing 2022;40(4):889
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Patchin JW, Hinduja S, Meldrum RC. Digital self‐harm and suicidality among adolescents. Child and Adolescent Mental Health 2023;28(1):52
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Pan W, Wang X, Zhou W, Hang B, Guo L. Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches. International Journal of Environmental Research and Public Health 2023;20(3):2688
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Jin H, Nath SS, Schneider S, Junghaenel D, Wu S, Kaplan C. An informatics approach to examine decision-making impairments in the daily life of individuals with depression. Journal of Biomedical Informatics 2021;122:103913
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Liu J, Shi M, Jiang H. Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion. International Journal of Environmental Research and Public Health 2022;19(13):8197
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Lyu S, Ren X, Du Y, Zhao N. Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Frontiers in Psychiatry 2023;14
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