Crime rate detection using social media of different crime locations and Twitter part-of-speech tagger with Brown clustering

Article type: Research Article

Authors: Vo, Thanh^a | Sharma, Rohit^b | Kumar, Raghvendra^c | Son, Le Hoang^d | Pham, Binh Thai^e | Tien Bui, Dieu^{f; g; *} | Priyadarshini, Ishaani^h | Sarkar, Manashⁱ | Le, Tuong^j

Affiliations: [a] Institute of Research and Development, Duy Tan University, Da Nang, Vietnam | [b] Department of Electronics & Communication Engineering, SRM IST, NCR Campus, India | [c] Department of Computer Science and Engineering, GIET University, Gunupur, India | [d] VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam | [e] Geotechnical Engineering and Artificial Intelligence research group (GEOAI), University of Transport Technology, Hanoi, Vietnam | [f] Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [g] Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [h] University of Delaware, Newark, DE, USA | [i] School of Computer Science& Engineering, Vellore Institute of Technology, Vellore, India | [j] Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

Correspondence: [*] Corresponding author. Dieu Tien Bui. E-mail: [email protected].

Abstract: Nowadays, the crime rate increases dramatically in every country. Therefore, it is an urgent need for governments and social associations to produce persistent solutions and disincentive penalties to prevent crime issues. Specifically, social media plays an important role in crime rate detection; thus, reducing crime rates significantly. It would be a good medium for the desired task. In this paper, we analyze Twitter data collected from Twitter accounts for seven different locations (Ghaziabad, Chennai, Bangaluru, Chandigarh, Jammu, Gujarat, and Hyderabad) from January 2014 to November 2018 in a case study of India, which is opted to illustrate the efficiency of the proposed work. Sentiment analysis has been used to analyze users’ behavior and psychology through the tweets of people to track crime actions. Twitter part-of-speech tagger, which is a Markov Model of first-order entropy, has been used for part-of-speech in online conversational text. Brown clustering is used for a long set of unlabeled tweets. Comparisons are verified with real crime rates from an authorized source of information according to different locations. We also measure the latest crime trends for the highest (Ghaziabad, Uttar Pradesh) and lowest crime cities (Jammu) in India. It has been found that the latest crime trends have been recorded for the time duration of 7 days (23, January 2019 to 30, January 2019). The analyses demonstrate that the obtained results match with the real crime rate data. We believe that these types of studies will help to detect the real-time crime rate for different locations and detect the crime pattern easily.

Keywords: Crime detection, Twitter data, social media, sentiment analysis, Brown clustering

DOI: 10.3233/JIFS-190870

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4287-4299, 2020