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

Abstract

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.

Different Ways Ambient and Immobile Population Distributions Influence Urban Crime Patterns

“…The dependent variables are the counts of each crime type. The independent variables are twofold: (1) tweet count as an ambient population measure [11,12,14,31,43], and ( 2) young (≤15) and elderly (>65) population as a measure of people with limited mobility, hereafter referred to as the immobile population [59]. The control socioeconomic variables are population density, population assisted by the Municipal Family Assistance Center, unemployed population, demographic load index (an age-structure indicator calculating the percentage of non-productive age population), and count of liquor stores in 2015 [52,[60][61][62][63][64][65].…”

“…Ristea and colleagues (2017) used tweets count as an explanatory variable to test the crime-tweets relationship [42]. Lan and colleagues had done serial studies to test the crime-tweets relationship and suggested the reliability of tweets as a feasible dynamic population measure [12,31,43]. Hipp and colleagues (2018) also found that tweets can help explain the presence of crime in California [11].…”