Youth Gun Violence Prevention in a Digital Age

Patton, Desmond Upton; McGregor, Kyle A.; Slutkin, Gary

doi:10.1542/peds.2017-2438

Cited by 15 publications

(17 citation statements)

References 5 publications

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“…For example, the features of social media, which include text, images, video, emojis, and hashtags can be used for automatic identification of behaviors, topics, and conversations that connote the intent to commit violence or discussion of past violent events and experiences. Importantly, social media may also provide deep insights into root causes and pathways to violence by showing connections to mental and physical health, neighborhood engagement, and proximity to psychosocial resources and city-wide support from local politicians (Patton et al 2018). Along these lines, computer scientists, social scientists, and community groups should work together to create inclusive and ethical AI systems that can thematically group social media posts regarding relevant events and conversations into categories (with particular attention to those which seem to promote violence or cyberbullying).…”

Section: Preventionmentioning

confidence: 99%

When Twitter Fingers Turn to Trigger Fingers: a Qualitative Study of Social Media-Related Gang Violence

Patton

Pyrooz

Decker

et al. 2019

Int Journal of Bullying Prevention

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Mounting evidence suggests that social media can exacerbate tensions among gangs that ultimately lead to violence, but serious questions remain about precisely how conflict online translates to conflict offline. The purpose of this study is to examine the ways in which gang violence can be mediated by the Internet. We conducted a sociolinguistic study with 17 Black males between the ages of 14-24 who self-identified at the time of the study as having current or former gang involvement to determine how online provocations may generate offline violence. We examine the sociolinguistic patterns of two prominent gangs on Chicago's South Side and use qualitative interviews and a vignette methodology to gather in-depth information into the nature of Internetmediated gang violence from multiple perspectives. We identified three forms of social media communication that were interpreted as threating by participants: dissing, calling, and direct threats. We developed a framework for understanding participant responses to tweets and the potential for violence that is a consequence of such posts. Lastly, we highlight racial decoding and importance of context when interpreting the social media communication of Black and Latino youth. This study has important implications for the prevention of gang violence that is amplified by social media communication. Findings can be used to initiate conversations between researchers and practitioners regarding the role of social media for prevention and the ethical use of such tools, particularly for marginalized populations.

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Section: Preventionmentioning

confidence: 99%

When Twitter Fingers Turn to Trigger Fingers: a Qualitative Study of Social Media-Related Gang Violence

Patton

Pyrooz

Decker

et al. 2019

Int Journal of Bullying Prevention

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“…Next, there are concerns about profiling. Although law enforcement in the United States, at least, already utilize social media strategies to reduce homicide rates, the use of social media data can be punitive and lead to ethical concerns regarding the impact of surveillance on communities (Patton et al, 2018). Algorithms lack the ability to accurately interpret off-line context, and there has been recognition by researchers that this could lead to the prejudicial treatment of marginalized communities.…”

Section: Risk Predictionmentioning

confidence: 99%

“…In application to the problem of violence, the use of social media data has been largely confined to natural language processing. Using these methods, social media data can provide interventionists with substantial amounts of information about marginalized communities at risk for violence (Patton et al, 2018).…”

Section: Using Social Media and Other Internet Data For Violence Prevmentioning

confidence: 99%

Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

Hunt

Tomlinson

Sikander

et al. 2020

Front. Artif. Intell.

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Violence against children is a global public health threat of considerable concern. At least half of all children worldwide experience violence every year; globally, the total number of children between the ages of 2 and 17 years who have experienced violence in any given year is one billion. Based on a review of the literature, we argue that there is substantial potential for AI (and associated machine learning and big data), and mHealth approaches to be utilized to prevent and address violence at a large scale. This potential is particularly marked in low-and middle-income countries (LMIC), although whether it could translate into effective solutions at scale remains unclear. We discuss possible entry points for Artificial Intelligence (AI), big data, and mHealth approaches to violence prevention, linking these to the World Health Organization's seven INSPIRE strategies. However, such work should be approached with caution. We highlight clear directions for future work in technology-based and technology-enabled violence prevention. We argue that there is a need for good agent-based models at the level of entire cities where and when violence can occur, where local response systems are. Yet, there is a need to develop common, reliable, and valid population-and individual/family-level data on predictors of violence. These indicators could be integrated into routine health or other information systems and become the basis of Al algorithms for violence prevention and response systems. Further, data on individual help-seeking behavior, risk factors for child maltreatment, and other information which could help us to identify the parameters required to understand what happens to cause, and in response to violence, are needed. To respond to ethical issues engendered by these kinds of interventions, there must be concerted, meaningful efforts to develop participatory and user-led work in the AI space, to ensure that the privacy and profiling concerns outlined above are addressed explicitly going forward. Finally, we make the case that developing AI and other technological infrastructure will require substantial investment, particularly in LMIC.

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“…Internet banging broadly includes online communications that promote gang affiliation, communicate threats, and share information about rivals to sympathetic local or national groups. These online communications, which are often inflammatory, disrespectful, and threatening toward rivals, have been blamed for inciting real-world violence (Patton et al, 2018a). Where gang conflict in the past may have escalated due to face-to-face encounters, the immediacy of online communications may result in increased conflict and escalation (Patton et al, 2018a).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These online communications, which are often inflammatory, disrespectful, and threatening toward rivals, have been blamed for inciting real-world violence (Patton et al, 2018a). Where gang conflict in the past may have escalated due to face-to-face encounters, the immediacy of online communications may result in increased conflict and escalation (Patton et al, 2018a). Patton and colleagues (2016a) interviewed individuals who work for community violence interruption groups and found that social media plays an increasing role in escalation of conflict between rival gangs.…”

Section: Literature Reviewmentioning

confidence: 99%

Guns on social media: complex interpretations of gun images posted by Chicago youth

Patton¹,

Frey²,

Gaskell³

2019

Palgrave Commun

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How should we interpret gun images on social media? Take for example the shooting at Stoneman Douglas High School. Media articles revealed that the gunman, a white adolescent male, posted images of firearms and other weapons on his social media profile prior to the shooting. On the other hand, when it comes to Black communities, digital policing strategies often intercept images with guns and individuals thought to be associated with gangs before a crime is ever committed. In this study, we use a mixed methods approach, situated in social systems theory, to make meaning of gun posting behavior among Black youth who associate with gangs in Chicago. We collected and examined a corpus of Twitter images (1851) through snowball sampling of a well-known deceased gang member in Chicago and users in their Twitter network. We identified 560 images that contain guns and asked two distinct groups to annotate images: formerly gang-involved outreach workers, known as community domain experts, at a local Chicago violence prevention organization and Master of Social Work students at Columbia University. After comparing their results, findings highlighted the prevalence and frequency of gun image posting within this corpus and critical differences in how community domain experts and social work annotators perceive guns. The various underlying intents provide a rich source of knowledge for understanding the symbolic nature of guns in the digital age.

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Youth Gun Violence Prevention in a Digital Age

Cited by 15 publications

References 5 publications

When Twitter Fingers Turn to Trigger Fingers: a Qualitative Study of Social Media-Related Gang Violence

When Twitter Fingers Turn to Trigger Fingers: a Qualitative Study of Social Media-Related Gang Violence

Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

Guns on social media: complex interpretations of gun images posted by Chicago youth

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