Mining Twitter data for crime trend prediction

Aghababaei, Somayyeh; Makrehchi, Masoud

doi:10.3233/ida-163183

Cited by 23 publications

(13 citation statements)

References 33 publications

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“…However, the spatial coverage of these geotagged tweets matches that of the crimes in the study area (Figures 2 and 3). In addition, model results clearly underscore the reliability of the tweets count as a measure of the ambient population as suggested by earlier studies [15,33,35,36,41,79,104,105].…”

Section: Discussionsupporting

confidence: 75%

The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

et al. 2019

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As a measurement of the residential population, the Census population ignores the mobility of the people. This weakness may be alleviated by the use of ambient population, derived from social media data such as tweets. This research aims to examine the degree in which geotagged tweets, in contrast to the Census population, can explain crime. In addition, the mobility of Twitter users suggests that tweets as the ambient population may have a spillover effect on the neighboring areas. Based on a yearlong geotagged tweets dataset, negative binomial regression models are used to test the impact of tweets derived ambient population, as well as its possible spillover effect on theft crimes. Results show: (1) Tweets count is a viable replacement of the Census population for spatial theft pattern analysis; (2) tweets count as a measure of the ambient population shows a significant spillover effect on thefts, while such spillover effect does not exist for the Census population; (3) the combination of tweets and its spatial lag outperforms the Census population in theft crime analyses. Therefore, the spillover effect of the tweets derived ambient population should be considered in future crime analyses. This finding may be applicable to other social media data as well.

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Section: Discussionsupporting

confidence: 75%

The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

et al. 2019

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“…Their model achieved an 𝐹 -measure of 0.83 for crimes such as theft, burglary, and sex offences; however, the results for crimes such as murder and vandalism correlated poorly. The same work was later extended with a temporal topic model [6] which outperformed the batch model in 17 out of 22 crime types. Vo et al [139] analyzed Twitter data from seven major cities of India to confirm that tweets contribute to a better understanding of crime rates.…”

Section: Crimementioning

confidence: 99%

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

2022

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The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the “big picture”—the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime , Disasters , Finance , Physical Environment , Politics , and Health and Population . We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.

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“…A good example of is the use of geotagged (also datetime stamped) data from Google Street View, Twitter, and Foursquare being analyzed by Y. using the geolocation to assign the data (visual, textual, and human behavior, respectively) to street-segments. Various approaches employ Natural Language Processing and text mining techniques for topic detection over corpuses of twitter data: Aghababaei and Makrehchi (2018) develop a temporal topic detection model to infer topics predictive of crime trends over time; Gerber (2014) uses kernel density estimation combined with statistical topic modeling to identify discussion topics for crime prediction; Kansara et al (2016) classify the sentiments of tweets using Naive Bayes to predict whether a person will become a threat to society. Other approaches analyze the links contained in social media using SNA, for instance (Hollywood et al, 2018).…”

Section: Open Data and Social Datamentioning

confidence: 99%

Themes in data mining, big data, and crime analytics

Oatley

2021

WIREs Data Min & Knowl

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This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools

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Mining Twitter data for crime trend prediction

Cited by 23 publications

References 33 publications

The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

Themes in data mining, big data, and crime analytics

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