Computational methods to understand deviant mental wellness communities

Stevie Chancellor

doi:10.1145/3170427.3173021

Computational methods to understand deviant mental wellness communities

Stevie Chancellor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

My research uses computational methods to understand deviant online communities to assess their health and well-being. My prior work studies the pro-eating disorder community, a specific deviant community that glorifies disordered eating behaviors. In my dissertation, I expand on this work in three ways: to help moderators manage deviant mental wellness content, to understand normative behaviors of support in communities, and ethical issues of predicting individualized mental wellness. Understanding these behaviors at-scale can help medical research develop better intervention strategies through social media as well as understanding bad behavior to make better online communities.

Original language	English (US)
Title of host publication	CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
Subtitle of host publication	Engage with CHI
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450356206, 9781450356213
DOIs	https://doi.org/10.1145/3170427.3173021
State	Published - Apr 20 2018
Externally published	Yes
Event	2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018 - Montreal, Canada Duration: Apr 21 2018 → Apr 26 2018

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings
Volume	2018-April

Other

Other	2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018
Country/Territory	Canada
City	Montreal
Period	4/21/18 → 4/26/18

Bibliographical note

Publisher Copyright:
Copyright held by the owner/author(s).

Keywords

Deviant behavior
Eating disorders
Machine learning
Mental health
Social media

Access

10.1145/3170427.3173021

OpenUrl availability

Full text

Cite this

Chancellor, S. (2018). Computational methods to understand deviant mental wellness communities. In CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI Article DC05 (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2018-April). Association for Computing Machinery. https://doi.org/10.1145/3170427.3173021

Computational methods to understand deviant mental wellness communities. / Chancellor, Stevie.
CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery, 2018. DC05 (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chancellor, S 2018, Computational methods to understand deviant mental wellness communities. in CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI., DC05, Conference on Human Factors in Computing Systems - Proceedings, vol. 2018-April, Association for Computing Machinery, 2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018, Montreal, Canada, 4/21/18. https://doi.org/10.1145/3170427.3173021

@inproceedings{0b23c22f21b74284aa20fa6f7f979790,

title = "Computational methods to understand deviant mental wellness communities",

abstract = "My research uses computational methods to understand deviant online communities to assess their health and well-being. My prior work studies the pro-eating disorder community, a specific deviant community that glorifies disordered eating behaviors. In my dissertation, I expand on this work in three ways: to help moderators manage deviant mental wellness content, to understand normative behaviors of support in communities, and ethical issues of predicting individualized mental wellness. Understanding these behaviors at-scale can help medical research develop better intervention strategies through social media as well as understanding bad behavior to make better online communities.",

keywords = "Deviant behavior, Eating disorders, Machine learning, Mental health, Social media",

author = "Stevie Chancellor",

note = "Publisher Copyright: Copyright held by the owner/author(s).; 2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018 ; Conference date: 21-04-2018 Through 26-04-2018",

year = "2018",

month = apr,

day = "20",

doi = "10.1145/3170427.3173021",

language = "English (US)",

series = "Conference on Human Factors in Computing Systems - Proceedings",

publisher = "Association for Computing Machinery",

booktitle = "CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems",

}

TY - GEN

T1 - Computational methods to understand deviant mental wellness communities

AU - Chancellor, Stevie

N1 - Publisher Copyright: Copyright held by the owner/author(s).

PY - 2018/4/20

Y1 - 2018/4/20

N2 - My research uses computational methods to understand deviant online communities to assess their health and well-being. My prior work studies the pro-eating disorder community, a specific deviant community that glorifies disordered eating behaviors. In my dissertation, I expand on this work in three ways: to help moderators manage deviant mental wellness content, to understand normative behaviors of support in communities, and ethical issues of predicting individualized mental wellness. Understanding these behaviors at-scale can help medical research develop better intervention strategies through social media as well as understanding bad behavior to make better online communities.

AB - My research uses computational methods to understand deviant online communities to assess their health and well-being. My prior work studies the pro-eating disorder community, a specific deviant community that glorifies disordered eating behaviors. In my dissertation, I expand on this work in three ways: to help moderators manage deviant mental wellness content, to understand normative behaviors of support in communities, and ethical issues of predicting individualized mental wellness. Understanding these behaviors at-scale can help medical research develop better intervention strategies through social media as well as understanding bad behavior to make better online communities.

KW - Deviant behavior

KW - Eating disorders

KW - Machine learning

KW - Mental health

KW - Social media

UR - http://www.scopus.com/inward/record.url?scp=85052019226&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052019226&partnerID=8YFLogxK

U2 - 10.1145/3170427.3173021

DO - 10.1145/3170427.3173021

M3 - Conference contribution

AN - SCOPUS:85052019226

T3 - Conference on Human Factors in Computing Systems - Proceedings

BT - CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018

Y2 - 21 April 2018 through 26 April 2018

ER -

Computational methods to understand deviant mental wellness communities

Abstract

Publication series

Other

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this