Dataset Information

Measuring Place Connectivity Using Big Social Media Data.

ABSTRACT: Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a multiscale, spatiotemporal-continuous, and easy-to-implement measurement. The proposed PCI, established and demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10 percent penetration in the US population) and Facebook social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong state boundary effect and that it generally follows the distance decay effect, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: 1) modeling the spatial spread of a contagious disease (e.g., COVID-19), and 2) modeling hurricane evacuation destination choices. The methodological and contextual knowledge of PCI, together with the launched visualization platform and data sharing capability, is expected to support research fields requiring knowledge in human spatial interactions.

SUBMITTER: Li Z

PROVIDER: S-EPMC7872361 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data.

Li Zhenlong Z Huang Xiao X Ye Xinyue X Jiang Yuqin Y Martin Yago Y Ning Huan H Hodgson Michael E ME Li Xiaoming X

ArXiv 20210208

Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactio ...[more]

PMID: 33564697

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Project description:BACKGROUND:Substance use-related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use-related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems. OBJECTIVE:The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction. METHODS:First, we provided a narrative summary of the literature on drug use-related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of "infodemiology and infoveillance" in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use-related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology. RESULTS:We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use-related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use-related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied. CONCLUSIONS:We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems.

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Dataset Information

Measuring Place Connectivity Using Big Social Media Data.

Publications

Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data.

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