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Cited by 13Mental Health via WritingGrief, Bereavement, and Mental HealthComputational and Text Analysis Methods

A human-machine partnered approach for identifying social media signals of elevated traumatic grief in Chicago gang territories

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Stuart, Forrest, Riley, Alicia, Pourreza, Hossein·2020·PLOS ONE
DOI 10.1371/journal.pone.0236625PMC PMC7392535
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Abstract

There is a critical need to improve trauma-informed services in structurally marginalized communities impacted by violence and its associated traumatic grief. For community residents, particularly gang-associated youth, repeated exposure to traumatic grief causes serious adverse effects that may include negative health outcomes, delinquency, and future violent offenses. The recent proliferation of digital social media platforms, such as Twitter, provide a novel and largely underutilized resource for responding to these issues, particularly among these difficult-to-reach communities. In this paper, we explore the potential for using a human-machine partnered approach, wherein qualitative fieldwork and domain expertise is combined with a computational linguistic analysis of Twitter content among 18 gang territories/neighborhoods on Chicago's South Side. We first employ in-depth interviews and observations to identify common patterns by which residents in gang territories/neighborhoods express traumatic grief on social media. We leverage these qualitative findings, supplemented by domain expertise and computational techniques, to gather both traumatic grief- and gang-related tweets from Twitter. We next utilize supervised machine learning to construct a binary classification algorithm to eliminate irrelevant tweets that may have been gathered by our automated query and extraction techniques. Last, we confirm the validity, or ground truth, of our computational findings by enlisting additional domain expertise and further qualitative analyses of the specific traumatic events discussed in our sample of Twitter content. Using this approach, we find that social media provides useful signals for identifying moments of increased collective traumatic grief among residents in gang territories/neighborhoods. This is the first study to leverage Twitter to systematically ground the collective online articulations of traumatic grief in traumatic offline events occurring in violence-impacted communities. The results of this study will be useful for developing more effective tools-including trauma-informed intervention applications-for community organizations, violence prevention initiatives, and other public health efforts.

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Research works printed but not linked: 3

  1. [9]Abreu A, Acker A. Context and collection: A research agenda for small data. In: iConference 2013 proceedings. Fort Worth, Texas; 2013. pp. 549–554.
  2. [11]Sered D. Until we reckon: Violence, mass incarceration, and a road to repair. New York, NY: The New Press; 2019.
  3. [12]Rich JA. Wrong place, wrong time: Trauma and violence in the lives of young black men. Baltimore, MD: John Hopkins University Press; 2009.

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Authors
Stuart, F., Riley, A., & Pourreza, H.
Affiliations
  • Stanford University (US)
  • University of California, San Francisco (US)
  • University of Chicago (US)
Corresponding author
Stuart, Forrest
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-07-30
Volume
15
Issue
7
Pages
e0236625
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0236625
PMC
PMC7392535
OpenAlex
W3046853450
Cited by
13
References
51
Citations by year
2026: 3 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 1
FWCI
2.19
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • University of Chicago (cc-by)
Funders
  • University of Chicago Women's Board
  • University of Chicago Urban Health Initiative
Topics
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Social Sciences › Psychology › Clinical Psychology › Grief, Bereavement, and Mental Health
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
traumatic grief, social media, Twitter, human-machine collaboration, computational linguistics, trauma-informed services, violence prevention, Chicago South Side, collective grief, tweet classification
MeSH terms
Algorithms, Chicago, Grief, Humans, Residence Characteristics, Violence, Social Media

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