Literature DB >> 36189428

The Relationship Between Social Media Data and Crime Rates in the United States.

Yan Wang1, Wenchao Yu1, Sam Liu2, Sean D Young1.   

Abstract

Crime monitoring tools are needed for public health and law enforcement officials to deploy appropriate resources and develop targeted interventions. Social media, such as Twitter, has been shown to be a feasible tool for monitoring and predicting public health events such as disease outbreaks. Social media might also serve as a feasible tool for crime surveillance. In this study, we collected Twitter data between May and December 2012 and crime data for the years 2012 and 2013 in the United States. We examined the association between crime data and drug-related tweets. We found that tweets from 2012 were strongly associated with county-level crime data in both 2012 and 2013. This study presents preliminary evidence that social media data can be used to help predict future crimes. We discuss how future research can build upon this initial study to further examine the feasibility and effectiveness of this approach.

Entities:  

Keywords:  Twitter; county; crime; social media; substance abuse

Year:  2019        PMID: 36189428      PMCID: PMC9524287          DOI: 10.1177/2056305119834585

Source DB:  PubMed          Journal:  Soc Media Soc        ISSN: 2056-3051


  20 in total

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Authors:  Sean D Young; William G Cumberland; Sung-Jae Lee; Devan Jaganath; Greg Szekeres; Thomas Coates
Journal:  Ann Intern Med       Date:  2013-09-03       Impact factor: 25.391

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Authors:  Panagiotis T Metaxas; Eni Mustafaraj
Journal:  Science       Date:  2012-10-26       Impact factor: 47.728

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Authors:  Sam Liu; Sean D Young
Journal:  J Forensic Leg Med       Date:  2016-10-30       Impact factor: 1.614

5.  Violent Crime and Park Use in Low-Income Urban Neighborhoods.

Authors:  Bing Han; Deborah A Cohen; Kathryn P Derose; Jiang Li; Stephanie Williamson
Journal:  Am J Prev Med       Date:  2018-01-12       Impact factor: 5.043

6.  Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?

Authors:  Jay M Ver Hoef; Peter L Boveng
Journal:  Ecology       Date:  2007-11       Impact factor: 5.499

7.  The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic.

Authors:  Alessio Signorini; Alberto Maria Segre; Philip M Polgreen
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

8.  Trending now: using social media to predict and track disease outbreaks.

Authors:  Charles W Schmidt
Journal:  Environ Health Perspect       Date:  2012-01       Impact factor: 9.031

9.  Monitoring Freshman College Experience Through Content Analysis of Tweets: Observational Study.

Authors:  Sam Liu; Miaoqi Zhu; Sean D Young
Journal:  JMIR Public Health Surveill       Date:  2018-01-11

10.  Social media use in the United States: implications for health communication.

Authors:  Wen-ying Sylvia Chou; Yvonne M Hunt; Ellen Burke Beckjord; Richard P Moser; Bradford W Hesse
Journal:  J Med Internet Res       Date:  2009-11-27       Impact factor: 5.428

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