Mental Health Analysis via Social Media Data

被引:15
作者
Yazdavar, Amir Hossein [1 ]
Mahdavinejad, Mohammad Saied [1 ]
Bajaj, Goonmeet [1 ]
Thirunarayan, Krishnaprasad [1 ]
Pathak, Jyotishman [2 ]
Sheth, Amit [1 ]
机构
[1] Wright State Univ, Kno E Sis Ctr, Dayton, OH 45435 USA
[2] Cornell Univ, Div Hlth Informat, New York, NY 10021 USA
来源
2018 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI) | 2018年
基金
美国国家卫生研究院;
关键词
Multi-modal Analysis; Machine Learning; Natural Language Processing; Statistical analysis; Social Media; Mental Health; Regression;
D O I
10.1109/ICHI.2018.00102
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With ubiquity of social media platforms, millions of people are routinely sharing their moods, feelings and even their daily struggles with mental health issues by expressing it verbally or indirectly through images they post. In this study, we aim to examine exploitation of big multi-modal social media data for studying depressive behavior and its population trend across the U.S. to better understand a regions influence on the prevailing environment and available care. In particular, employing statistical techniques along with the fusion of heterogeneous features gleaned from different modalities (shared images and textual content), we build models to detect depressed individuals and their demographics.
引用
收藏
页码:459 / 460
页数:2
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