Exploring Twitter to Analyze the Public’s Reaction Patterns to Recently Reported Homicides in London

Abstract

Crime is an ubiquitous part of society. The way people express their concerns about crimes has been of particular interest to the scientific community. Over time, the numbers and kinds of available communication channels have increased. Today, social media services, such Twitter, present a convenient way to express opinions and concerns about crimes. The main objective of this study is to explore people’s perception of homicides, specifically, how the characteristics and proximity of the event affect the public’s concern about it. The analysis explores Twitter messages that refer to homicides that occurred in London in 2012. In particular, the dependence of tweeting propensity on the proximity, in space and time, of a crime incident and of people being concerned about that particular incident are examined. Furthermore, the crime characteristics of the homicides are analysed using logistic regression analysis. The results show that the proximity of the Twitter users’ estimated home locations to the homicides’ locations impacts on whether the associated crime news is spread or not and how quickly. More than half of the homicide related tweets are sent within the first week and the majority of them are sent within a month of the incident’s occurrence. Certain crime characteristics, including the presence of a knife, a young victim, a British victim, or a homicide committed by a gang are predictors of the crime-tweets posting frequency.

Crime and its fear in social media

“…However, with social media, we could obtain an almost immediate reflection of the fear of crime in a city. Users of social media might express their concerns and fears of crime more frequently in a more dangerous city, enabling us to ARTICLE PALGRAVE COMMUNICATIONS | https://doi.org/10.1057/s41599-020-0430-7 understand how we arrive at our perception of security (Kounadi et al, 2015).…”

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

“…To counterbalance the tweets discrepancies and restrict the impact that “heavy” users may have in our models, only one location per user was selected. According to Kounadi et al (Kounadi, Lampoltshammer, Groff, Sitko, & Leitner, 2015), the spatial distribution of tweets by user in the city of London, UK, was found to be highly clustered for most of the users. We tested a sample of 50 randomly selected users and found similar patterns.…”

Mapping the popularity of urban restaurants using social media data

“…Yang and Mu (2015) detected depressed tweets and analyzed the spatial pattern depression by calculating Moran's I index. Kounadi et al (2015) used the "Nearest Neighbor Hierarchical Clustering" to examine the proximity dependency of the Twitter responses to homicide responses. In another application, the geolocated nighttime tweet data were used as ancillary information to perform the areal interpolation of population (Lin & Cromley, 2015).…”

“…This has not only changed the traditional form of news reporting but also provided new opportunities for geographic science (Sui & Goodchild, 2011), given the rich geographic information attached to the social media data, often known as "geotags," in the form of longitude and latitude coordinates (Croitoru, Wayant, Crooks, Radzikowski, & Stefanidis, 2014;Lin & Cromley, 2015;Shelton, Poorthuis, Graham, & Zook, 2014). Scholars have applied social media data, Facebook, and microblogging, for example, into many fields of applied geographic studies, including population migration, urban space pattern, commuting behaviors, environmental event reactions, pandemics and disaster predictions, and crime occurrence (Cao et al, 2015;Char & Stow, 2015;Chunara, Andrews, & Brownstein, 2012;Croitoru et al, 2014;Gerber, 2014;Jang & Hart, 2015;Kounadi, Lampoltshammer, Groff, Sitko, & Leitner, 2015;Lampoltshammer, Kounadi, Sitko, & Hawelka, 2014;Lin & Cromley, 2015;Shelton et al, 2014;Patel & Jermacane, 2015;Widener & Li, 2014).…”