@article{TEXTUAL,
      recid = {6211},
      author = {Stuart, Forrest and Riley, Alicia and Pourreza, Hossein},
      title = {A human-machine partnered approach for identifying social  media signals of elevated traumatic grief in Chicago gang  territories},
      journal = {PLOS ONE},
      address = {2020-07-30},
      number = {TEXTUAL},
      abstract = {<p>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.</p>},
      url = {http://knowledge.uchicago.edu/record/6211},
}