Academic Appointments

  • Associate Professor, Sociology

Program Affiliations

  • American Studies

Contact

  • Academic
    fstuart@stanford.edu
    University - Faculty Department: Sociology Position: Assoc Professor

Additional Info

  • Mail Code: 2047

2020-21 Courses

Stanford Advisees

  • Doctoral Dissertation Reader (AC)
    Catherine Sirois
  • Doctoral Dissertation Co-Advisor (AC)
    Rebecca Gleit
  • Doctoral (Program)
    Kimya Loder, Caylin Moore

All Publications

  • Whose Lives Matter? Race, Space, and the Devaluation of Homicide Victims in Minority Communities SOCIOLOGY OF RACE AND ETHNICITY White, K., Stuart, F., Morrissey, S. L. 2020

    View details for DOI 10.1177/2332649220948184

    View details for Web of Science ID 000572380000001

  • Code of the Tweet: Urban Gang Violence in the Social Media Age SOCIAL PROBLEMS Stuart, F. 2020; 67 (2): 191–207

    View details for DOI 10.1093/socpro/spz010

    View details for Web of Science ID 000537434000001

  • A human-machine partnered approach for identifying social media signals of elevated traumatic grief in Chicago gang territories. PloS one Stuart, F. n., Riley, A. n., Pourreza, H. n. 2020; 15 (7): e0236625

    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.

    View details for DOI 10.1371/journal.pone.0236625

    View details for PubMedID 32730354