A Scorecard Method for Detecting Depression in Social Media Users

被引:0
|
作者
Tefera, Netsanet [1 ]
Zhou, Lina [1 ]
机构
[1] Univ Maryland Baltimore Cty, Baltimore, MD 21228 USA
基金
美国国家科学基金会;
关键词
STATES; RISK;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Depression is one of the most prevalent mental health disorders today. Depression has become the leading causes of disability and premature mortality partly due to a lack of effective methods for early detection. This research explores how social media can be used as a tool to detect the level of depression in its users by proposing a scorecard method based on their user profiles. In the proposed method, depression is measured by a selected set of key dimensions along with their specific indicators, which are weighted based on their importance for signaling depression in the literature. To evaluate the scorecard method, we compared three types of social media users: users who committed suicide due to depression, users who were likely suffering from depression, and users who were unlikely suffering from depression. The empirical results demonstrate the effectiveness of the scorecard method in detecting depression.
引用
收藏
页码:554 / 563
页数:10
相关论文
共 50 条
  • [31] Social Media Users Potentially Experience Different Withdrawal Symptoms to Non-social Media Users
    Roberto Truzoli
    Lorena Magistrati
    Caterina Viganò
    Stella Conte
    Lisa A. Osborne
    Phil Reed
    International Journal of Mental Health and Addiction, 2023, 21 : 411 - 417
  • [32] Detecting Personality Traces in Users' Social Activity
    Kleanthous, Styliani
    Herodotou, Constantinos
    Samaras, George
    Germanakos, Panayiotis
    SOCIAL COMPUTING AND SOCIAL MEDIA, SCSM 2016, 2016, 9742 : 287 - 297
  • [33] Towards Detecting Influential Users in Social Networks
    Rad, Amir Afrasiabi
    Benyoucef, Morad
    E-TECHNOLOGIES: TRANSFORMATION IN A CONNECTED WORLD, 2011, 78 : 227 - 240
  • [34] Plight of Social Media Users: The Problem of Fake News on Social Media
    Alkawaz, Mohammed Hazim
    Khan, Sayeed Ahsan
    Abdullah, Muhammad Irsyad
    11TH IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE 2021), 2021, : 289 - 293
  • [35] A web sentiment analysis method on fuzzy clustering for mobile social media users
    Li Yang
    Xinyu Geng
    Haode Liao
    EURASIP Journal on Wireless Communications and Networking, 2016
  • [36] Recommending Users and Communities in Social Media
    Li, Lei
    Peng, Wei
    Kataria, Saurabh
    Sun, Tong
    Li, Tao
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2015, 10 (02)
  • [37] Motivation in Teenage Users of Social Media
    Fyodorov, V. V.
    Mileev, I. D.
    SOCIAL PSYCHOLOGY AND SOCIETY, 2015, 6 (03) : 98 - 108
  • [38] A Multi-View Learning Approach for Detecting Personality Disorders Among Arab Social Media Users
    Duwairi, Rehab
    Halloush, Zain
    ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2023, 22 (04)
  • [39] Marketing Activities among Social Media Users in Cambodia: Mixed Method Research
    Em, Oussa
    Makmee, Pattrawadee
    Wongupparaj, Peera
    FWU JOURNAL OF SOCIAL SCIENCES, 2023, 17 (01): : 47 - 61
  • [40] A web sentiment analysis method on fuzzy clustering for mobile social media users
    Yang, Li
    Geng, Xinyu
    Liao, Haode
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2016,