The perception of security refers to the subjective evaluation of risks related to security events and the magnitude of their consequences. Negative perceptions have severe implications in society. These feelings are commonly quantified by citizen's surveys, which are time-consuming and do not adapt well to the changing dynamics linked to security. Recently, Twitter social network emerged as an alternative to quantify some of these feelings dynamically. However, most of these approaches focused on counting the amount of content related to crime, which is only one of the relevant factors determining the perception of security. In addition, these models do not account for the polarity and tone underlying the social network content. This work introduces a model for quantification of Perception of Security on Twitter based on sentiment analysis and studies its relationship with actual crime. The model relies on an automatic strategy for filtering content related to security based on a support vector machine classifier and quantifying the sentiment in the posts using a multinomial naive Bayes classifier. These measures of sentiment allow computing estimates of perception of security at a daily scale. This model studied more than 1.700.000 tweets in Bogotá (Colombia), collected during more than one year (March 18 of 2019 -April 28 of 2020). Results suggest that machine learning-based approaches may outperform previous security content filtering strategies by 21% in F1-score and provide quantifications on the sentiments of about 40% on accuracy. Trained models were used to provide daily estimates of perception of security, which are different from those captured by counting the number of posts related to security. Furthermore, the associations between perceptions and actual crimes exhibited monthly variations and showed that in some months, perceptions were more related to robbery, a crime with high incidence in the city. These results may help decisionmakers devise strategies to reduce the impact of the negative perception on citizens.
Aggressive behaviors are violent actions or disputes that one individual effectuates over another in which physical harm might happen and occurs in a social environment. These criminal events have negative consequences for public health and citizen's security, especially in Latin American cities. Predictive crime aims to use analytical techniques on crime databases to identify potential criminal activity. Most research focuses on other types of crime, such as homicide and crime against property. However, there is little research to describe predictive patterns for aggressive behavior at the city scale. This paper studies possible sessional patterns of aggressive behavior crime and its relationship with temporal dynamics shared across different city areas in Bogotá (Colombia), a Latin American city severely affected by this phenomenon. For this, we propose a Spatio-temporal analysis strategy based on predictability, a grounded information theory measure of sessionality, and independent component analysis. Using this approach, we studied more than three million registers reported to the city emergency line from 2014 to 2018 related to aggressive behaviors. Our results show that many city areas exhibit high sessionality values and share multiple temporal dynamics in 8 of 19 regions (localities). Notably, most of these areas present both patterns in 7 of 19 regions. Remarkably, these patterns emerged in regions that account for the 71% of aggressive behavior reports. These results agree with modern crime theories that consider Spatio-temporal dynamics, such as routine activity theory, suggesting that the citizen's routines may generate particular social dynamics which significantly influence aggressive behavior.INDEX TERMS aggressive behavior, predictive security, routine activity theory of crime, sessionality, predictability, independent component analysis (ICA).
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