Relationships between crime and Twitter activity around stadiums

Ristea, Alina; Langford, Chad; Leitner, Michael

doi:10.1109/geoinformatics.2017.8090933

Cited by 13 publications

(13 citation statements)

References 14 publications

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“…A similar technique, detecting the use of words such as "food" or "wedding" allowed the construction of a real-time measure of happiness (Dodds et al, 2011), which showed weekly and daily cycles of happiness. In terms of crime, detecting whether a tweet includes words such as "violence", was used to classify tweets as "crime-tweets" (Ristea et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Reports to fix street disorders, such as graffiti, were successfully used as signals correlated with fear of crime . Also, by detecting tweets which contain at least one violence-related word, the attitudes of the audience around stadiums were analysed (Ristea et al, 2017). In a similar manner, the tweets from a news agency were analysed to "predict" hit-and-run crimes in a city in the USA (Wang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Crime and its fear in social media

et al. 2020

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Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crime and its fear in social media

et al. 2020

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“…Previous studies have shown it is possible to extract useful information from football related Twitter data via semantic analyses using simple subsets from specific dictionaries [25], naïve Bayes models, random forests, logistic regression, and support vector machine [26,27]. These earlier empirical studies motivated the research question in this paper.…”

Section: Data Mining Social Media For Sporting Event Informationmentioning

confidence: 96%

Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

Ristea

Kurland

Resch

et al. 2018

IJGI

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Abstract:Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime.

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“…A model to expose how right-wing politics is less tolerant over specific topics was revealed in (Ottoni et al, 2018). A correlation of violence and crime near stadiums in England influenced by tweets was analyzed (Ristea, Langford and Leitner, 2017 In summary, information coming from the Twitter site can be relevant in many domains related to violence, as we see in Table 1. Moreover, perception analysis through Twitter is an area of opportunity to establish preventive activities to mitigate or decrease the vulnerable sensation of security.…”

Section: Related Workmentioning

confidence: 99%

An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico

Gutiérrez

Sánchez-Cervantes

Paredes-Valverde

2020

REVISTA PERSPECT. EMPRESARIAL

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This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan

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Relationships between crime and Twitter activity around stadiums

Cited by 13 publications

References 14 publications

Crime and its fear in social media

Crime and its fear in social media

Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico

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