Are Crime and Collective Emotion Interrelated? A “Broken Emotion” Conjecture from Community Twitter Posts

A neighborhood’s social cohesion, referring to the emotional and social connection of people within it, tends to have an influential impact on its crime level. Traditional approaches to measuring social cohesion and collective efficacy are mostly interviews and surveys, which are usually costly in t...

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Bibliographic Details
Published inSocial science computer review Vol. 41; no. 2; pp. 528 - 553
Main Authors Lan, Minxuan, Liu, Lin, Burmeister, Jacob, Zhu, Weili, Zhou, Hanlin, Gu, Xin
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.04.2023
SAGE PUBLICATIONS, INC
Subjects
Cohesion
Crime
Criminology
Data collection
Economic factors
Effectiveness
Emotions
Information
Land use
Mass media effects
Neighborhoods
Psychological theories
Psychology
Robbery
Signal detection
Social cohesion
Social media
Socioeconomic factors
Theft
collective efficacy
social cohesion
tweets
collective emotion
neighborhood crime
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Summary:A neighborhood’s social cohesion, referring to the emotional and social connection of people within it, tends to have an influential impact on its crime level. Traditional approaches to measuring social cohesion and collective efficacy are mostly interviews and surveys, which are usually costly in time, money, and other resources. Big social media data provides us with a new and cost-effective source of such information. We believe the combination of spatial and contextual information of geotagged Twitter posts (tweets) can gauge the residents’ collective emotions in a neighborhood. The positivity and negativity of these collective emotions may be used to approximate the collective efficacy of the community. Inspired by the broken window theory, we propose a broken emotion conjecture to explain the relationship between collective emotion and crime. To test this conjecture, we collected data on four types of crime (assaults, burglaries, robberies, and thefts) and all public geotagged tweets (N = 778,901) in Cincinnati, Ohio, USA in 2013. We extracted innovative variables from tweets’ spatial and contextual information to explain community crime and enlighten new criminology theory. Results of negative binomial models show: (1) with necessary socio-economic and land-use factors controlled, the more negative the collective emotion of a neighborhood, the more the crime (except for theft); (2) however, the positivity of the collective emotion of a neighborhood does not have any statistically significant influence on crime. These correspond well with signal detection theory in psychology. The proposed broken emotion conjecture is supported with data from Cincinnati and its general applicability should be tested in other regions.
ISSN:0894-4393
1552-8286
DOI:10.1177/08944393221113210