Academic Appointments
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Professor, Sociology
Program Affiliations
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American Studies
2024-25 Courses
- Junior Seminar: Preparation for Research
URBANST 202A (Win) - The Sociology of Music
AMSTUD 4, CSRE 4, SOC 4 (Aut) - Workshop: Qualitative and Fieldwork Methods
SOC 380W (Aut, Win, Spr) -
Independent Studies (11)
- Coterminal MA directed research
SOC 291 (Aut, Win, Spr, Sum) - Coterminal MA individual study
SOC 290 (Aut, Win, Spr, Sum) - Coterminal MA research apprenticeship
SOC 292 (Aut, Win, Spr, Sum) - Curricular Practical Training
SOC 392 (Aut, Win, Spr, Sum) - Graduate Directed Research
SOC 391 (Aut, Win, Spr, Sum) - Graduate Individual Study
SOC 390 (Aut, Win, Spr, Sum) - Senior Honors Thesis
URBANST 199 (Aut, Win, Spr, Sum) - Senior Thesis
SOC 196 (Aut, Win, Spr, Sum) - Undergraduate Directed Research
SOC 191 (Aut, Win, Spr, Sum) - Undergraduate Individual Study
SOC 190 (Aut, Win, Spr, Sum) - Undergraduate Research Apprenticeship
SOC 192 (Aut, Win, Spr, Sum)
- Coterminal MA directed research
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Prior Year Courses
2023-24 Courses
- Ethnographic and Fieldwork Methods
SOC 376 (Spr) - Music and Society: Perspectives from Berlin
OSPBER 55 (Aut) - Workshop: Qualitative and Fieldwork Methods
SOC 380W (Win, Spr)
2021-22 Courses
- Ethnographic and Fieldwork Methods
SOC 376 (Spr) - The Sociology of Music
AFRICAAM 4, AMSTUD 4, CSRE 4, SOC 4 (Win) - Workshop: Qualitative and Fieldwork Methods
SOC 380W (Win, Spr)
- Ethnographic and Fieldwork Methods
Stanford Advisees
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Doctoral Dissertation Advisor (AC)
Caylin Moore -
Doctoral (Program)
Lorena Aviles Trujillo, Caylin Moore, Marisol Zarate
All Publications
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Gang Research in the Twenty-First Century
ANNUAL REVIEW OF CRIMINOLOGY
2022; 5: 299-320
View details for DOI 10.1146/annurev-criminol-030920-094656
View details for Web of Science ID 000789890200014
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Addressing urban disorder without police: How Seattle's LEAD program responds to behavioral-health-related disruptions, resolves business complaints, and reconfigures the field of public safety
LAW & POLICY
2021
View details for DOI 10.1111/lapo.12178
View details for Web of Science ID 000729325500001
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Policing gentrification or policing displacement? Testing the relationship between order maintenance policing and neighbourhood change in Los Angeles
URBAN STUDIES
2021
View details for DOI 10.1177/0042098021993354
View details for Web of Science ID 000656016800001
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Whose Lives Matter? Race, Space, and the Devaluation of Homicide Victims in Minority Communities
SOCIOLOGY OF RACE AND ETHNICITY
2020
View details for DOI 10.1177/2332649220948184
View details for Web of Science ID 000572380000001
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Code of the Tweet: Urban Gang Violence in the Social Media Age
SOCIAL PROBLEMS
2020; 67 (2): 191–207
View details for DOI 10.1093/socpro/spz010
View details for Web of Science ID 000537434000001
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A human-machine partnered approach for identifying social media signals of elevated traumatic grief in Chicago gang territories.
PloS one
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