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Social network analysis as a tool for understanding mass shooting prevention: A case study of the Marjory Stoneman Douglas High School shooting

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  • Greene-Colozzi, Emily A.
  • Schildkraut, Jaclyn
  • Arrigo, Lucia
  • Krebs, Lauryn
  • Klein, Brent R.

Abstract

This study presents a social network analysis of the pre-incident social and behavioral environment surrounding the perpetrator of the 2018 shooting at Marjory Stoneman Douglas (MSD) High School in Parkland, Florida. Social network analysis is a valuable tool for visualizing and understanding social ties and relations among entities and has been applied across social science disciplines to study deviant and legitimate social groups. We developed an ego network for the MSD perpetrator to understand who comprised his social network and the warning signs these network members both observed and reported. Results indicate that nearly all network members observed at least one type of concerning behavior, but only about half reported them to authorities. Gun-related behaviors and physical aggression and violence were predominantly observed. The network itself was sparse and had little cohesion, and influential actors within the network may have restricted the flow of information rather than facilitated its sharing. These findings suggest several directions for future research aimed at preventative policy, including development of centralized reporting systems, specialized awareness training for families, students, and teachers, and continued exploration of the egocentric social networks of mass public shooters and plotters to uncover the attributes of successfully prevented cases.

Suggested Citation

  • Greene-Colozzi, Emily A. & Schildkraut, Jaclyn & Arrigo, Lucia & Krebs, Lauryn & Klein, Brent R., 2025. "Social network analysis as a tool for understanding mass shooting prevention: A case study of the Marjory Stoneman Douglas High School shooting," Journal of Criminal Justice, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:jcjust:v:101:y:2025:i:c:s0047235225001898
    DOI: 10.1016/j.jcrimjus.2025.102540
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    References listed on IDEAS

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    1. Gary A. Ackerman & Lauren E. Pinson, 2016. "Speaking Truth to Sources: Introducing a Method for the Quantitative Evaluation of Open Sources in Event Data," Studies in Conflict and Terrorism, Taylor & Francis Journals, vol. 39(7-8), pages 617-640, July.
    2. Bright, David & Whelan, Chad & Jones, Callum & Edson-Wilkinson, Kelly, 2025. "The utility of social network analysis to examine conflict and collaboration across boundaries: A review and research agenda for Outlaw Motorcycle Gangs," Journal of Criminal Justice, Elsevier, vol. 97(C).
    3. David Décary-Hétu & Benoit Dupont, 2012. "The social network of hackers," Global Crime, Taylor & Francis Journals, vol. 13(3), pages 160-175, August.
    4. Lucia Cavallaro & Annamaria Ficara & Pasquale De Meo & Giacomo Fiumara & Salvatore Catanese & Ovidiu Bagdasar & Wei Song & Antonio Liotta, 2020. "Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
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