Published on 12.05.15 in Vol 2, No 2 (2015): April-June
Works citing "Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model"
According to Crossref, the following articles are citing this article (DOI 10.2196/mental.4227):
(note that this is only a small subset of citations)
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Cheng Q, Kwok C, Zhu T, Guan L, Yip P. Suicide Communication on Social Media and Its Psychological Mechanisms: An Examination of Chinese Microblog Users. International Journal of Environmental Research and Public Health 2015;12(9):11506
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Fodeh SJ, Boudreaux ED, Wang R, Silva D, Bossarte R, Goulet JL, Brandt C, Altalib HH. Suicide Risk on Twitter. International Journal of Knowledge Discovery in Bioinformatics 2018;8(2):1
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Braithwaite SR, Giraud-Carrier C, West J, Barnes MD, Hanson CL. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality. JMIR Mental Health 2016;3(2):e21
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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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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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Haines-Delmont A, Chahal G, Bruen AJ, Wall A, Khan CT, Sadashiv R, Fearnley D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR mHealth and uHealth 2020;8(6):e15901
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Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
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Li A, Jiao D, Liu X, Sun J, Zhu T. A Psycholinguistic Analysis of Responses to Live-Stream Suicides on Social Media. International Journal of Environmental Research and Public Health 2019;16(16):2848
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Zu X, Diao X, Meng Z. The impact of social media input intensity on firm performance: Evidence from Sina Weibo. Physica A: Statistical Mechanics and its Applications 2019;536:122556
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Li A, Jiao D, Zhu T. Detecting depression stigma on social media: A linguistic analysis. Journal of Affective Disorders 2018;232:358
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Cheng Q, Li TM, Kwok C, Zhu T, Yip PS. Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study. Journal of Medical Internet Research 2017;19(7):e243
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Coşkun M, Ozturan M. #europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study). Information 2018;9(5):102
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Liu X, Liu X, Sun J, Yu NX, Sun B, Li Q, Zhu T. Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors. Journal of Medical Internet Research 2019;21(5):e11705
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Khasawneh A, Chalil Madathil K, Dixon E, Wiśniewski P, Zinzow H, Roth R. Examining the Self-Harm and Suicide Contagion Effects of the Blue Whale Challenge on YouTube and Twitter: Qualitative Study. JMIR Mental Health 2020;7(6):e15973
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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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Wongkoblap A, Vadillo MA, Curcin V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research 2017;19(6):e228
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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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Aladağ AE, Muderrisoglu S, Akbas NB, Zahmacioglu O, Bingol HO. Detecting Suicidal Ideation on Forums: Proof-of-Concept Study. Journal of Medical Internet Research 2018;20(6):e215
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Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. International Journal of Environmental Research and Public Health 2020;17(16):5929
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Hettige NC, Nguyen TB, Yuan C, Rajakulendran T, Baddour J, Bhagwat N, Bani-Fatemi A, Voineskos AN, Mallar Chakravarty M, De Luca V. Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach. General Hospital Psychiatry 2017;47:20
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Wang X, Chen S, Li T, Li W, Zhou Y, Zheng J, Chen Q, Yan J, Tang B. Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis. JMIR Medical Informatics 2020;8(7):e17958
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Taylor J, Pagliari C. Mining social media data: How are research sponsors and researchers addressing the ethical challenges?. Research Ethics 2018;14(2):1
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Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1)
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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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Acuña Caicedo RW, Gómez Soriano JM, Melgar Sasieta HA. Assessment of supervised classifiers for the task of detecting messages with suicidal ideation. Heliyon 2020;6(8):e04412
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Bruen AJ, Wall A, Haines-Delmont A, Perkins E. Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users. JMIR Mental Health 2020;7(9):e18407
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Ji S, Pan S, Li X, Cambria E, Long G, Huang Z. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. IEEE Transactions on Computational Social Systems 2021;8(1):214
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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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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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Acuña Caicedo RW, Gómez Soriano JM, Melgar Sasieta HA. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519
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Bonifazi G, Cecchini S, Corradini E, Giuliani L, Ursino D, Virgili L. Extracting time patterns from the lifespans of TikTok challenges to characterize non-dangerous and dangerous ones. Social Network Analysis and Mining 2022;12(1)
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Yang BX, Xia L, Liu L, Nie W, Liu Q, Li XY, Ao MQ, Wang XQ, Xie YD, Liu Z, Huang YJ, Huang Z, Gong X, Luo D. A Suicide Monitoring and Crisis Intervention Strategy Based on Knowledge Graph Technology for “Tree Hole” Microblog Users in China. Frontiers in Psychology 2021;12
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. Detecting Suicide Ideation in the Online Environment: A Survey of Methods and Challenges. IEEE Transactions on Computational Social Systems 2022;9(3):679
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Lao C, Lane J, Suominen H. Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study. JMIR Formative Research 2022;6(8):e35563
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Wang R, Yang BX, Ma Y, Wang P, Yu Q, Zong X, Huang Z, Ma S, Hu L, Hwang K, Liu Z. Medical-Level Suicide Risk Analysis: A Novel Standard and Evaluation Model. IEEE Internet of Things Journal 2021;8(23):16825
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Cao L, Zhang H, Feng L. Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media. IEEE Transactions on Multimedia 2022;24:87
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Taghvaei N, Masoumi B, Keyvanpour MR. Analytical framework for mental health feature extraction methods in social networks. Intelligent Decision Technologies 2021;15(3):343
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Safa R, Bayat P, Moghtader L. Automatic detection of depression symptoms in twitter using multimodal analysis. The Journal of Supercomputing 2022;78(4):4709
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Zhao Y, Liu D, Wan C, Liu X, Qiu X, Nie J. Find Supports for the Post about Mental Issues: More Than Semantic Matching. ACM Transactions on Asian and Low-Resource Language Information Processing 2022;21(6):1
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Cao L, Zhang H, Wang X, Feng L. Learning Users Inner Thoughts and Emotion Changes for Social Media Based Suicide Risk Detection. IEEE Transactions on Affective Computing 2023;14(2):1280
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Geng S, He Y, Duan L, Yang C, Wu X, Liang G, Niu B. The Association Between Linguistic Characteristics of Physicians’ Communication and Their Economic Returns: Mixed Method Study. Journal of Medical Internet Research 2024;26:e42850
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According to Crossref, the following books are citing this article (DOI 10.2196/mental.4227):
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Huang Y, Liu X, Zhu T. Human Centered Computing. 2019. Chapter 17:166
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Su Y, Zheng H, Liu X, Zhu T. Human Centered Computing. 2019. Chapter 26:257
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Zhu S, Wang X, Liu P. Chinese Lexical Semantics. 2021. Chapter 34:408
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Guo LY, Xia L, Huang XY, Fu YX, Li XY, Zhou SC, Zhao C, Yang BX. Health Information Science. 2022. Chapter 17:177
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Wongkoblap A, Vadillo MA, Curcin V. Mental Health in a Digital World. 2022. :109
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Liang Z, Liu D, Wan Q, Liu X, Liao G, Wan C. Social Media Processing. 2024. Chapter 4:48
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