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Social Network Research Collaborative

The SNRC uses a combination of network analysis and interdisciplinary social science frameworks and methods to conduct applied research in public health, education, and a variety of other domains

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The University of South Florida Social Network Research Collaborative seeks to advance network research in the domains of public health and education. We blend qualitative and quantitative analyses with emerging methodologies such as natural language processing and artificial intelligence. This keeps us on the cutting edge of research and allows us to inform both an academic as well as applied audiences. 

Integrated Public Health and Social Networks

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Public health and social networks are interconnected in a dialectical relationship; in which social networks influence health outcomes while disease/illness impact individual social networks. Social networks may shape access to information and care received, cognitive processes associated with healing or maintenance of conditions, etc. By understanding these connections and relationships, it may be possible to use social networks prescriptively to positively influence health outcomes. 

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Networks, Education, and Health Interventions

Medical research

Educational outcomes are intimately linked to social networks, comprised of students, peers, educators, administration, etc.- all of which influences academic success. These embedded networks are vital to understand as they influence performance and retention in academic settings. This is particularly important in areas such as STEM, which seek to diversify the student body as well as increase overall enrollment. 

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Network Science and Innovations in AI

AI Network

Artificial Intelligence (AI) is being rapidly adopted across scientific disciplines, and the field of social network analysis is no exception. Specifically, how we can leverage AI methods such as natural language processing, machine learning, graph algorithms, to analyze social network data. These methods allow us to streamline the data collection process, increase the power of statistical tools to detect changes in network structure or composition, and examine associations between networks and outcomes of interest. Understanding these dynamics utilizing AI methods, we can understand how social dynamics impact health and educational outcomes.

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At the Forefront of Research

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