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
  • [21] Leveraging Multiple Characterizations of Social Media Users for Depression Detection Using Data Fusion
    Maria Valencia-Segura, Karla
    Jair Escalante, Hugo
    Villasenor-Pineda, Luis
    PATTERN RECOGNITION, MCPR 2022, 2022, 13264 : 215 - 224
  • [22] Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media
    Alvari, Hamidreza
    Shaabani, Elham
    Sarkar, Soumajyoti
    Beigi, Ghazaleh
    Shakarian, Paulo
    COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), 2019, : 154 - 161
  • [23] Correction to: Detecting social media users based on pedestrian networks and neighborhood attributes: an observational study
    Victor H. Masias
    Tobias Hecking
    Fernando Crespo
    H. Ulrich Hoppe
    Applied Network Science, 4
  • [24] A Novel Improved BILSTM Method For Depression Detection On Social Media
    Jin, Shangzhu
    Tang, Zheng
    Peng, Jun
    Han, Xiaoqi
    2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024, 2024,
  • [25] Detecting Depression from Social Media Data as a Multiple-Instance Learning Task
    Mann, Paulo
    Matsushima, Elton H.
    Paes, Aline
    2022 10TH INTERNATIONAL CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII), 2022,
  • [26] Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review
    Liu, Danxia
    Feng, Xing Lin
    Ahmed, Farooq
    Shahid, Muhammad
    Guo, Jing
    JMIR MENTAL HEALTH, 2022, 9 (03):
  • [27] Method for Detecting Far-Right Extremist Communities on Social Media
    Karpova, Anna
    Savelev, Aleksei
    Vilnin, Alexander
    Kuznetsov, Sergey
    SOCIAL SCIENCES-BASEL, 2022, 11 (05):
  • [28] Detecting Addiction, Anxiety, and Depression by Users Psychometric Profiles
    Monreale, Anna
    Iavarone, Benedetta
    Rossetto, Elena
    Beretta, Andrea
    COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, 2022, : 1189 - 1197
  • [29] BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster
    Lin, Xiaotian
    Fu, Yingwen
    Yang, Ziyu
    Lin, Nankai
    Jiang, Shengyi
    PROCEEDINGS OF THE SECOND WORKSHOP ON LANGUAGE TECHNOLOGY FOR EQUALITY, DIVERSITY AND INCLUSION (LTEDI 2022), 2022, : 200 - 205
  • [30] Social Media Users Potentially Experience Different Withdrawal Symptoms to Non-social Media Users
    Truzoli, Roberto
    Magistrati, Lorena
    Vigano, Caterina
    Conte, Stella
    Osborne, Lisa A.
    Reed, Phil
    INTERNATIONAL JOURNAL OF MENTAL HEALTH AND ADDICTION, 2023, 21 (01) : 411 - 417