Exploring the dominant features of social media for depression detection

Jamil Hussain; Fahad Ahmed Satti; Muhammad Afzal; Wajahat Ali Khan; Hafiz Syed Muhammad Bilal; Muhammad Zaki Ansaar; Hafiz Farooq Ahmad; Taeho Hur; Jaehun Bang; Jee In Kim; Gwang Hoon Park; Hyonwoo Seung; Sungyoung Lee

doi:10.1177/0165551519860469

Exploring the dominant features of social media for depression detection

Jamil Hussain, Fahad Ahmed Satti, Muhammad Afzal, Wajahat Ali Khan, Hafiz Syed Muhammad Bilal, Muhammad Zaki Ansaar, Hafiz Farooq Ahmad, Taeho Hur, Jaehun Bang, Jee In Kim, Gwang Hoon Park^*, Hyonwoo Seung, Sungyoung Lee^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

50 Citations (SciVal)

Original language	English
Pages (from-to)	739-759
Number of pages	21
Journal	Journal of Information Science
Volume	46
Issue number	6
DOIs	https://doi.org/10.1177/0165551519860469
Publication status	Published (VoR) - 1 Dec 2020

Funding

The authors are grateful to myPersonality.org for providing the dataset used in this paper. This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support programme (IITP-2017-0-01629) supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP). This work was supported by an IITP grant funded by the Korean government (MSIT) (no.2017-0-00655).

Keywords

Depression
Facebook
mental illness
value-added information

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10.1177/0165551519860469

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title = "Exploring the dominant features of social media for depression detection",

keywords = "Depression, Facebook, mental illness, value-added information",

author = "Jamil Hussain and Satti, \{Fahad Ahmed\} and Muhammad Afzal and Khan, \{Wajahat Ali\} and Bilal, \{Hafiz Syed Muhammad\} and Ansaar, \{Muhammad Zaki\} and Ahmad, \{Hafiz Farooq\} and Taeho Hur and Jaehun Bang and Kim, \{Jee In\} and Park, \{Gwang Hoon\} and Hyonwoo Seung and Sungyoung Lee",

note = "Funding Information: The authors are grateful to myPersonality.org for providing the dataset used in this paper. This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support programme (IITP-2017-0-01629) supervised by the Institute of Information \& Communications Technology Planning \& Evaluation (IITP). This work was supported by an IITP grant funded by the Korean government (MSIT) (no.2017-0-00655). Publisher Copyright: {\textcopyright} The Author(s) 2019.",

year = "2020",

month = dec,

day = "1",

doi = "10.1177/0165551519860469",

language = "English",

volume = "46",

pages = "739--759",

journal = "Journal of Information Science",

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TY - JOUR

T1 - Exploring the dominant features of social media for depression detection

AU - Hussain, Jamil

AU - Satti, Fahad Ahmed

AU - Afzal, Muhammad

AU - Khan, Wajahat Ali

AU - Bilal, Hafiz Syed Muhammad

AU - Ansaar, Muhammad Zaki

AU - Ahmad, Hafiz Farooq

AU - Hur, Taeho

AU - Bang, Jaehun

AU - Kim, Jee In

AU - Park, Gwang Hoon

AU - Seung, Hyonwoo

AU - Lee, Sungyoung

N1 - Funding Information: The authors are grateful to myPersonality.org for providing the dataset used in this paper. This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support programme (IITP-2017-0-01629) supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP). This work was supported by an IITP grant funded by the Korean government (MSIT) (no.2017-0-00655). Publisher Copyright: © The Author(s) 2019.

PY - 2020/12/1

Y1 - 2020/12/1

KW - Depression

KW - Facebook

KW - mental illness

KW - value-added information

UR - https://www.scopus.com/pages/publications/85071444992

UR - https://www.scopus.com/inward/citedby.url?scp=85071444992&partnerID=8YFLogxK

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DO - 10.1177/0165551519860469

M3 - Article

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SN - 0165-5515

VL - 46

SP - 739

EP - 759

JO - Journal of Information Science

JF - Journal of Information Science

IS - 6

ER -

Exploring the dominant features of social media for depression detection

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