Mining Social Media Content for Crime Prediction

Aghababaei, Somayyeh; Makrehchi, Masoud

doi:10.1109/wi.2016.0089

Cited by 45 publications

(19 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding broadly supports the work of other studies e.g., in epidemiology there is an extensive number of studies dedicated to these ideas [39][40][41][42][43]. In addition, few studies have investigated crime reports or rate prediction [15,[44][45][46].…”

Section: Discussionsupporting

confidence: 87%

“…In recent years, there has been an increasing interest in online social media monitoring and Big Data analytics. Social media platforms are being used to gather information and in some cases, to examine crime prediction [13][14][15]. However, much of the research up to now has only focused on cities from USA such as Chicago, Arizona, San Francisco and New York [16][17][18][19].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Exploring crime patterns in Mexico City

Piña-García

Ramírez

2019

J Big Data

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 87%

mentioning

confidence: 99%

Exploring crime patterns in Mexico City

Piña-García

Ramírez

2019

J Big Data

View full text Add to dashboard Cite

show abstract

“…One of the promising research direction is fusing social network data that can provide socio-behavior "signals" for crime prediction. While the hypothesis that publicly available data in social networks may include predictive variables for crime and security events [1], [3] is known, we argue that adding communitybased information will improve the prediction quality.…”

Section: Future Work and Conclusionmentioning

confidence: 89%

“…Although the data sources we proposed to work with are updated on-line (or at least there is always an option for the federal authorities to receive this information on-line), predicting crimes may benefit from features added from social networks as it has been shown in [1], [28], [14]. One of the promising research direction is fusing social network data that can provide socio-behavior "signals" for crime prediction.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

Spatio-temporal prediction of crimes using network analytic approach

Dash

Safro

Srinivasamurthy

2018

2018 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to cities. With the increase in urban population, it is important to keep an eye on criminal activities. By doing so, governments can enforce intelligent policing systems and hence many government agencies and local authorities have made the crime data publicly available. In this paper, we analyze Chicago city crime data fused with other social information sources using network analytic techniques to predict criminal activity for the next year. We observe that as we add more layers of data which represent different aspects of the society, the quality of prediction is improved. Our prediction models not just predict total number of crimes for the whole Chicago city, rather they predict number of crimes for all types of crimes and for different regions in City of Chicago. Reproducibility: our code is available at https://goo.gl/V1yVLk.

show abstract

“…Twitter has been used by researchers to make sense of human behavior. The behavior of users on this platform has been used in particular to assist prediction of criminal violence Gerber 2014;Aghababaei and Makrehchi 2016;Williams, Burnap, and Sloan 2017;Bendler et al 2014). The link between the prediction of social unrest and the user's online activity on Twitter has been studied by (Compton et al 2013).…”

Section: Background and Related Workmentioning

confidence: 99%

Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods

Keymanesh

Gurukar

Boettner

et al. 2020

Complex Networks XI

View full text Add to dashboard Cite

Sociologists associate the spatial variation of crime within an urban setting, with the concept of collective efficacy. The collective efficacy of a neighborhood is defined as social cohesion among neighbors combined with their willingness to intervene on behalf of the common good. Sociologists measure collective efficacy by conducting survey studies designed to measure individuals' perception of their community. In this work, we employ the curated data from a survey study (ground truth) and examine the effectiveness of substituting costly survey questionnaires with proxies derived from social media. We enrich a corpus of tweets mentioning a local venue with several linguistic and topological features. We then propose a pairwise learning to rank model with the goal of identifying a ranking of neighborhoods that is similar to the ranking obtained from the ground truth collective efficacy values. In our experiments, we find that our generated ranking of neighborhoods achieves 0.77 Kendall tau-x ranking agreement with the ground truth ranking. Overall, our results are up to 37% better than traditional baselines.

show abstract

Mining Social Media Content for Crime Prediction

Cited by 45 publications

References 16 publications

Exploring crime patterns in Mexico City

Exploring crime patterns in Mexico City

Spatio-temporal prediction of crimes using network analytic approach

Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods

Contact Info

Product

Resources

About