A human-machine partnered approach for identifying social media signals of elevated traumatic grief in Chicago gang territories

Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study

This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City's low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents' experiences of gun violence and provide important implications for gun violence prevention in a digital era.

How Americans encounter guns: Mixed methods content analysis of YouTube and internet search data

Firearm-related injury and death is a serious public health issue in the U.S. As more Americans consume news and media online, there is growing interest in using these channels to prevent firearm-related harms. Understanding the firearm-related narratives to which consumers are exposed is foundational to this work. This research used the browsing behavior of a representative sample of American adults to identify seven firearm-related content "ecosystems" (defined as naturally occurring networks of channels watched by the same users) on YouTube; we then described the demographics and internet search patterns of users affiliated with each ecosystem. Over the 9-month study period, 72,205 panelists had 16,803,075 person-video encounters with 7,274,093 videos. Among these, 282,419 were related to firearms. Using fast greedy clustering, we partitioned users and channel interactions into seven distinct channel-based content ecosystems that reached more than 1/1000 YouTube users per day. These ecosystems were diverse in reach, users, and content (e.g., guns for self-protection, guns for fun). On average, 0.5% of panelists performed a firearm-related internet search on a given day. The vast majority of searches were related to mass shootings or police-involved shootings (e.g., "active shooter"), and virtually none were about more common firearm harm such as suicide. Searches for firearm safety information were most common among panelists affiliated with the "Hunting & Fishing" and "Guns & Gear" ecosystems, which were watched primarily by older, white men. These findings identify an opportunity for analyzing firearm-related narratives and tailoring firearm safety messaging for users affiliated with specific online content ecosystems.