“…Specifically, we considered anonymizing gang names for confidentiality reasons. However, because this study is highly contextual, purging these dynamics would purge important contextualities from the study and severely limit our ability to build on other studies relating to Chicago gangs (e.g., Aspholm 2020;Papachristos 2009;Papachristos et al 2013;Patton et al 2017). We did take significant steps to protect the confidentiality of individuals who commented on the Facebook page.…”
Social media is increasingly intertwined into people’s lives, spurring questions about the relationships between online behavior and offline actions. We advance knowledge in conflict dynamics by using a multiplex network framework that conceptualizes online and offline gang relationships as co-constitutive networks—online and offline relationships often overlap and entangle in complex ways that influence behavior in both the virtual and real worlds. We propose a mixed-methods abductive approach for digital data that uses qualitative analyses to challenge and corroborate quantitative analyses of online gang conflict. Synthesizing data from Facebook posts by alleged gang members, maps of gang territory, and police records of offline shooting events, we show that online gang conflicts are not random attacks but targeted network relationships, and such online relationships are dependent on offline geographic relationships and shooting history relationships between gangs. Our mixed-methods approach further shows via qualitative analyses that the statistical network associations are based on cultural-specific language surrounding gang names and symbols, neighborhood streets, and prominent gang members. Our approach underscores how mixed-methods and qualitative approaches are essential in unpacking “big data” and computational methods in understanding the multiplex nature of group conflict.
“…Specifically, we considered anonymizing gang names for confidentiality reasons. However, because this study is highly contextual, purging these dynamics would purge important contextualities from the study and severely limit our ability to build on other studies relating to Chicago gangs (e.g., Aspholm 2020;Papachristos 2009;Papachristos et al 2013;Patton et al 2017). We did take significant steps to protect the confidentiality of individuals who commented on the Facebook page.…”
Social media is increasingly intertwined into people’s lives, spurring questions about the relationships between online behavior and offline actions. We advance knowledge in conflict dynamics by using a multiplex network framework that conceptualizes online and offline gang relationships as co-constitutive networks—online and offline relationships often overlap and entangle in complex ways that influence behavior in both the virtual and real worlds. We propose a mixed-methods abductive approach for digital data that uses qualitative analyses to challenge and corroborate quantitative analyses of online gang conflict. Synthesizing data from Facebook posts by alleged gang members, maps of gang territory, and police records of offline shooting events, we show that online gang conflicts are not random attacks but targeted network relationships, and such online relationships are dependent on offline geographic relationships and shooting history relationships between gangs. Our mixed-methods approach further shows via qualitative analyses that the statistical network associations are based on cultural-specific language surrounding gang names and symbols, neighborhood streets, and prominent gang members. Our approach underscores how mixed-methods and qualitative approaches are essential in unpacking “big data” and computational methods in understanding the multiplex nature of group conflict.
Behavioral science has a long history of engaging in efforts to understand and address socially important issues. Poverty and inequities in health and development are among the most important and complex social issues facing the world today. With its Sustainable Development Goals (SDGs), the United Nations (2015) has focused attention and guidance on addressing key global challenges, including to “end poverty” (SDG 1), “ensure good health and well-being for all” (SDG3), and “reduce inequality within and among countries” (SDG 10). In this paper, we provide a framework and illustrative examples of contributions of behavioral science to these issues. We feature illustrative behavioral interventions at the individual, relationship, community, and societal levels. We highlight the diversity of issues, intervention methods, and settings reflected in applications of behavioral science. By joining methods from behavioral science, public health, and other disciplines—and the experiential knowledge of those most affected by inequities—behavioral methods can make significant contributions to collaborative efforts to assure health and well-being for all.
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