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Detecting and characterizing eating-disorder communities on social media

Detecting and characterizing eating-disorder communities on social media
Detecting and characterizing eating-disorder communities on social media
Eating disorders are complex mental disorders and responsible for the highest mortality rate among mental illnesses. Recent studies reveal that user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studying communities of people who discuss eating disorders on social media, while few studies have explored community structures and interactions among individuals who suffer from this disease over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connections with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioral patterns and psychometric properties; 2. building predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of social connections between eating disordered individuals on Twitter, we present the first homophily study among eating-disorder communities on social media. Our findings shed new light on how an eating-disorder community develops on social media.
91-100
Wang, Tao
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Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Ianni, Antonella
35024f65-34cd-4e20-9b2a-554600d739f3
Mentzakis, Emmanouil
c0922185-18c7-49c2-a659-8ee6d89b5d74
Wang, Tao
c728baeb-cc3f-4948-bf1a-8d63ed60ea74
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Ianni, Antonella
35024f65-34cd-4e20-9b2a-554600d739f3
Mentzakis, Emmanouil
c0922185-18c7-49c2-a659-8ee6d89b5d74

Wang, Tao, Brede, Markus, Ianni, Antonella and Mentzakis, Emmanouil (2017) Detecting and characterizing eating-disorder communities on social media. Tenth ACM International Conference on Web Search and Data Mining, United Kingdom. 06 Feb 2016 - 10 Feb 2017 . pp. 91-100 . (doi:10.1145/3018661.3018706).

Record type: Conference or Workshop Item (Paper)

Abstract

Eating disorders are complex mental disorders and responsible for the highest mortality rate among mental illnesses. Recent studies reveal that user-generated content on social media provides useful information in understanding these disorders. Most previous studies focus on studying communities of people who discuss eating disorders on social media, while few studies have explored community structures and interactions among individuals who suffer from this disease over social media. In this paper, we first develop a snowball sampling method to automatically gather individuals who self-identify as eating disordered in their profile descriptions, as well as their social network connections with one another on Twitter. Then, we verify the effectiveness of our sampling method by: 1. quantifying differences between the sampled eating disordered users and two sets of reference data collected for non-disordered users in social status, behavioral patterns and psychometric properties; 2. building predictive models to classify eating disordered and non-disordered users. Finally, leveraging the data of social connections between eating disordered individuals on Twitter, we present the first homophily study among eating-disorder communities on social media. Our findings shed new light on how an eating-disorder community develops on social media.

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More information

Accepted/In Press date: 17 October 2016
e-pub ahead of print date: 6 February 2017
Venue - Dates: Tenth ACM International Conference on Web Search and Data Mining, United Kingdom, 2016-02-06 - 2017-02-10
Organisations: Economics

Identifiers

Local EPrints ID: 403262
URI: http://eprints.soton.ac.uk/id/eprint/403262
PURE UUID: 7d50f7fc-d909-4912-8812-8b577d0e6686
ORCID for Antonella Ianni: ORCID iD orcid.org/0000-0002-5003-4482
ORCID for Emmanouil Mentzakis: ORCID iD orcid.org/0000-0003-1761-209X

Catalogue record

Date deposited: 19 Dec 2016 14:24
Last modified: 07 Oct 2020 07:18

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