An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online

Manuel Saldaña

doi:10.29333/jisem/8492

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Manuel Saldaña ¹ ² ^*

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¹ Faculty of Engineering and Architecture, Universidad Arturo Prat, Almirante Juan José Latorre 2901, Antofagasta 1244260, CHILE
² Department of Computing and Systems Engineering, Universidad Católica de Norte, Angamos 0610, Antofagasta 1270709, CHILE
^* Corresponding Author

Abstract

Crime analysis represents a great challenge to law enforcement considering that the sources to use for generating intelligence are diverse in content and/or structure. However, in recent years, techniques such as natural language processing, a field of computing, artificial intelligence and linguistics have been developed that allow to study the interactions between computers and human language, and that in turn can be used effectively in the analysis of large amounts of texts and in the subsequent derivation of interesting analytical results. This paper presents a model for analysis of criminal events from online newspapers, identifying the areas with the highest crime rates through the detection and geographical mapping of critical points and the analysis of the nature of the criminal event. The evaluation of the proposed model to estimate the perception of crime in the domain of the proposed communes indicates that it is efficient in categorizing the news and the nature of these (validated by the performance indicators).

Keywords

criminal analysis
geographical mapping
perception of criminality
web mining

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.29333/jisem/8492

J INFORM SYSTEMS ENG, 2020 - Volume 5 Issue 4, Article No: em0126

Publication date: 30 Aug 2020

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References

Abdul Jalil, M. @ M., Ling, C. P., Mohamad Noor, N. M. and Mohd., F. (2017). Knowledge Representation Model for Crime Analysis. Procedia Comput. Sci., 116, 484-491. https://doi.org/10.1016/j.procs.2017.10.067
Achsan, H. T. Y. and Wibowo, W. C. (2014). A fast distributed focused-web crawling. Procedia Eng., 69, 492-499. https://doi.org/10.1016/j.proeng.2014.03.017
Adnan, M., Nagi, M., Kianmehr, K., Tahboub, R., Ridley, M. and Rokne, J. (2011). Promoting where, when and what? An analysis of web logs by integrating data mining and social network techniques to guide ecommerce business promotions. Soc. Netw. Anal. Min., 1(3), 173-185. https://doi.org/10.1007/s13278-010-0015-3
Alami, S. and Elbeqqali, O. (2015). Cybercrime profiling: Text mining techniques to detect and predict criminal activities in microblog posts. 2015 10th Int. Conf. Intell. Syst. Theor. Appl. SITA 2015. https://doi.org/10.1109/SITA.2015.7358435
Alhajj, R. and Rokne, J. (2018). Encyclopedia of Social Network Analysis and Mining. New York, NY: Springer New York.
Andrienko, G., Gunopulos, D., Ioannidis, Y., Kalogeraki, V., Katakis, I., Morik, K. and Verscheure, O. (2016). Mining Urban Data (Part B). Inf. Syst., 57, 75-76. https://doi.org/10.1016/j.is.2016.01.001
Armando, H., Eraso, O., Alberto, C. and Lozada, C. (2011). Stemming in the Spanish Language for Documents Recovered from the Web. (58), 107-114.
Arulanandam, R., Savarimuthu, B. T. R. and Purvis, M. A. (2014). Extracting Crime Information from Online Newspaper Articles. In Proceedings of the Second Australasian Web Conference (AWC 2014), Auckland, New Zealand, Awc, 31-38.
Badal-Valero, E., Alvarez-Jareño, J. A. and Pavía, J. M. (2018). Combining Benford’s Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Sci. Int., 282, 24–34. https://doi.org/10.1016/j.forsciint.2017.11.008
Belur, J. and Johnson, S. (2018). Is crime analysis at the heart of policing practice? A case study. Polic. Soc., 28(7), 768-786. https://doi.org/10.1080/10439463.2016.1262364
Boba, R. (2001). Introductory Guide to Crime Analysis and Mapping. Available at: http://www.ncjrs.gov/App/abstractdb/AbstractDBDetails.aspx?id=194685
Boba, R. (2016). Crime Analysis with Crime Mapping (4th Ed.). London, UK: SAGE Publications.
Cesur, R., Ceyhan, E. B., Kermen, A. and Sağiroğlu, Ş. (2017). Determination of potential criminals in social network. Gazi Univ. J. Sci., 30(1), 121–131.
Chen, H., Chung, W., Xu, J. J., Wang, G., Qin, Y. and Chau, M. (2004). Crime data mining: A general framework and some examples. Computer (Long. Beach. Calif)., 37(4), 50-56. https://doi.org/10.1109/MC.2004.1297301
Çinar, M. S., Genç, B. and Sever, H. (2019). Identifying criminal organizations from their social network structures. Turkish J. Electr. Eng. Comput. Sci., 27(1), 421–436. https://doi.org/10.3906/elk-1806-52
Dammert, L. and Lunecke, A. (2002). Victimización y Temor en Chile: Revisión Teórico Empírica en Doce Comunas del País. Santiago, Chile. Available at: https://www.cesc.uchile.cl/publicaciones/se_01_victimizacion.pdf
Daniel, J. and Martin, J. H. (2018). N-gram Language Models. In Speech and Language Processing (3rd Ed.), pp. 28.
Darwiche, A. (2009). Modeling and Reasoning with Bayesian Networks (1st Ed.). Los Angeles, USA: Cambridge University Press.
De Farias, A. M. G., Cintra, M. E., Felix, A. C. and Cavalcante, D. L. (2018). Definition of Strategies for Crime Prevention and Combat Using Fuzzy Clustering and Formal Concept Analysis. Int. J. Uncertainty, Fuzziness Knowlege-Based Syst., 26(3), 429–452. https://doi.org/10.1142/S0218488518500216
de Mendonça, R. R., de Brito, D. F., de Franco Rosa, F., dos Reis, J. C. and Bonacin, R. (2020). A framework for detecting intentions of criminal acts in social media: A case study on twitter. Inf., 11(3), 1-40. https://doi.org/10.3390/info11030154
De Sousa Netto, M. C., Pinto, A. L. and Semeler, A. R. (2019). Man and machines against crime: An approach based on visual learning. Educ. Inf., 35(3), 251–262. https://doi.org/10.3233/EFI-190280
Dowerah Baruah, T. (2012). Effectiveness of Social Media as a tool of communication and its potential for technology enabled connections: A micro-level study. Int. J. Sci. Res. Publ., 2(5), 1-10.
Dressel, J. and Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Sci. Adv., 4(1), 1–6. https://doi.org/10.1126/sciadv.aao5580
European Commission (2011). Crime and deviance in the EU. Brussels, Belgium. Available at: https://ec.europa.eu/research/social-sciences/pdf/policy_reviews/crime-and-deviance_en.pdf
Evans, J. M. and Kebbell, M. R. (2012). The effective analyst: A study of what makes an effective crime and intelligence analyst. Polic. Soc., 22(2), 204–219. https://doi.org/10.1080/10439463.2011.605130
Fundación Paz Ciudadana (2020). Índice Paz Ciudadana. Santiago, Chile. Available at: https://pazciudadana.cl/wp-content/uploads/2019/10/IPC-2019-Conferencia.pdf
Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decis. Support Syst., 61, 115-125. https://doi.org/10.1016/j.dss.2014.02.003
Goldstein Braunfeld, E. (2003). Los Robos con Violencia en el Gran Santiago: Magnitudes y Características. Santiago, Chile. Available at: https://www.cesc.uchile.cl/publicaciones/se_05_goldstein.pdf
Grosan, C. and Abraham, A. (2011). Intelligent Systems (1st Ed., vol. 17). Berlin, Heidelberg, Germany: Springer Berlin Heidelberg.
Hammouda, K. M. and Kamel, M. S. (2004) Efficient phrase-based document indexing for web document clustering. IEEE Trans. Knowl. Data Eng., 16(10), 1279-1296. https://doi.org/10.1109/TKDE.2004.58
Hotho, A., Nürnberger, A. and Paaß, G. (2005). A Brief Survey of Text Mining. Ldv Forum, 20(1), 19-62.
Instituto Nacional de Estadísticas (2020). Encuesta Nacional Urbana de Seguridad Ciudadana 2019. Santiago, Chile. Available at: http://www.seguridadpublica.gov.cl/enusc_2012.html
Jayaweera, I., Sajeewa, C., Liyanage, S., Wijewardane, T., Perera, I. and Wijayasiri, A. (2015). Crime analytics: Analysis of crimes through newspaper articles. MERCon 2015 - Moratuwa Eng. Res. Conf., pp. 277-282. https://doi.org/10.1109/MERCon.2015.7112359
Justicia de la Torre, C., Sánchez, D., Blanco, I. and Martín-Bautista, M. J. (2018). Text Mining: Techniques, Applications, and Challenges. Int. J. Uncertainty, Fuzziness Knowledge-Based Syst., 26(04), 553-582. https://doi.org/10.1142/S0218488518500265
Kahya-Özyirmidokuz, E. (2016). Analyzing unstructured Facebook social network data through web text mining: A study of online shopping firms in Turkey. Inf. Dev., 32(1), 70-80. https://doi.org/10.1177/0266666914528523
Kao, A. and Poteet, S. R. (2007). Natural Language Processing and Text Mining (1st Ed.). London, UK: Springer London.
Kaushik, A. and Naithani, S. (2016). A Comprehensive Study of Text Mining Approach. IJCSNS Int. J. Comput. Sci. Netw. Secur., 16(2), 69.
Kelleher, J., Mac Namee, B. and D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics (1st Ed.). London, UK: The MIT Press.
Kumar, S. N. (2015). World towards Advance Web Mining: A Review. Am. J. Syst. Softw., 3(2), 44-61. https://doi.org/10.12691/AJSS-3-2-3
Li, Y. and Zhong, N. (2004). Web mining model and its applications for information gathering. Knowledge-Based Syst., 17(5-6), 207-217. https://doi.org/10.1016/j.knosys.2004.05.002
Li, Y. S. and Qi, M. L. (2019). An approach for understanding offender modus operandi to detect serial robbery crimes. J. Comput. Sci., 36, 101024. https://doi.org/10.1016/j.jocs.2019.101024
Lim, C., Kim, K. J. and Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82(February), 86–99, 2018, https://doi.org/10.1016/j.cities.2018.04.011
Lim, M., Abdullah, A., Jhanjhi, N. Z. and Khurram Khan, M. (2020). Situation-Aware Deep Reinforcement Learning Link Prediction Model for Evolving Criminal Networks. IEEE Access, 8, 16550–16559. https://doi.org/10.1109/ACCESS.2019.2961805
Lim, M., Abdullah, A., Jhanjhi, N. Z. and Supramaniam, M. (2019a). Hidden link prediction in criminal networks using the deep reinforcement learning technique. Computers, 8(1), 1–13. https://doi.org/10.3390/computers8010008
Lim, M., Abdullah, A., Jhanjhi, N., Khurram Khan, M. and Supramaniam, M. (2019b). Link prediction in time-evolving criminal network with deep reinforcement learning technique. IEEE Access, 7, 184797–184807. https://doi.org/10.1109/ACCESS.2019.2958873
Lin, Y. L., Yen, M. F. and Yu, L. C. (2018). Grid-based crime prediction using geographical features. ISPRS Int. J. Geo-Information, 7(8), 298. https://doi.org/10.3390/ijgi7080298
Liu, B. (2011). Web Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg.
Majnik, M. and Bosnić, Z. (2013). ROC analysis of classifiers in machine learning: A survey. Intell. Data Anal., 17(3), 531-558. https://doi.org/10.3233/IDA-130592
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S. and Zeineddine, H. (2019). An Experimental Study with Imbalanced Classification Approaches for Credit Card Fraud Detection. IEEE Access, 7, 93010–93022. https://doi.org/10.1109/ACCESS.2019.2927266
Marinescu, V. and Balica, E. (2018). Migration and Crime Realities and Media Representations. Springer Nature Switzerland AG.
Mawby, R. I. (2004). Crime and Disorder: Perceptions of Business People in Cornwall, England. Int. Rev. Vict., 11(2-3), 313-332. https://doi.org/10.1177/026975800401100207
Mawby, R. I. (2015). Exploring the relationship between crime and place in the countryside *. J. Rural Stud., 39, 262-270. https://doi.org/10.1016/j.jrurstud.2014.12.003
Meijer, A. and Wessels, M. (2019). Predictive Policing: Review of Benefits and Drawbacks. Int. J. Public Adm., 42(12), 1031-1039. https://doi.org/10.1080/01900692.2019.1575664
Moghaddam, A. S., Hosseinkhani, J., Chuprat, S., Taherdoost, H. and Baravati, H. B. (2013). Proposing a framework for exploration of crime data using web structure and content mining. Res. J. Appl. Sci. Eng. Technol., 6(19), 3617-3624. http://doi.org/10.19026/rjaset.6.3568
Mukhopadhyay, D. (2019). Web Searching and Mining (1st Ed.). Singapore: Springer Singapore.
Nokhbeh Zaeem, R., Manoharan, M., Yang, Y. and Barber, K. S. (2017). Modeling and analysis of identity threat behaviors through text mining of identity theft stories. Comput. Secur., 65, 50–63. https://doi.org/10.1016/j.cose.2016.11.002
O’Shea, T. C. and Nicholls, K. (2002). Crime Analysis in America. Massachusetts, USA. Available at: https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/812146-commercialmdhd-truckfuelefficiencytechstudy-v2.pdf
Olavarría, M. (2006). El crimen en Chile Una mirada desde las víctimas. Santiago, Chile. Available at: http://www.cesc.uchile.cl/publicaciones/se_13_victimizacion.pdf
Pan, Y., Tian, Y., Liu, X., Gu, D. and Hua, G. (2016). Urban Big Data and the Development of City Intelligence. Engineering, 2(2), 171–178. https://doi.org/10.1016/J.ENG.2016.02.003
Petherick, W. (2015). Applied Crime Analysis (1st Ed.). Waltham, Massachusets, USA: Elsevier.
Pinto, P., Theodoro, I., Arrais, M. and Oliveira, J. (2017). Data mining and social web semantics: A case study on the use of hashtags and memes in Online Social Networks. IEEE Lat. Am. Trans., 15(12), 2276-2281. https://doi.org/10.1109/TLA.2017.8071088
Po, L. and Rollo, F. (2018). Building an Urban Theft Map by Analyzing Newspaper Crime Reports. In Proceedings - 13th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2018, pp. 13–18. https://doi.org/10.1109/SMAP.2018.8501866
Python Software Foundation (2019). “Requests: HTTP for HumansTM”. Available at: https://pypi.org/project/requests/ (Accessed: 11 July 2019).
Rapidminer.com (2018). Available at: https://rapidminer.com/products/ (Accessed: 27 June 2018).
Richardson, L. (2019). Beautiful Soup: We called him Tortoise because he taught us. Available at: https://www.crummy.com/software/BeautifulSoup/ (Accessed: 4 March 2020).
Rouvroy, A. (2016). Of Data and Men’ Fundamental Rights and Fredoms in a World of Data. Strasbourg, Germany. Available at: https://rm.coe.int/16806a6020
Saldaña, M., Escobar, C., Galvez, E., Torres, D. and Toro, N. (2020). Mapping of the Perception of Theft Crimes from Analysis of Newspaper Articles Online. In 15th Iberian Conference on Information Systems and Technologies (CISTI), June, pp. 1-7. https://doi.org/10.23919/CISTI49556.2020.9141154
Saldaña, M., Flores, V., Toro, N. and Leiva, C. (2019). Representation for a prototype of recommendation system of operation mode in copper mining. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), June, 1-4. https://doi.org/10.23919/CISTI.2019.8760884
Sanders, C. and Condon, C. (2017). Crime analysis and cognitive effects: the practice of policing through flows of data. Glob. Crime, 18(3), 237–255. https://doi.org/10.1080/17440572.2017.1323637
Sathyadevan, S., Devan, M. S. and Surya Gangadharan, S. (2014). Crime analysis and prediction using data mining. 1st Int. Conf. Networks Soft Comput. ICNSC 2014 - Proc., August, 406-412. https://doi.org/10.1109/CNSC.2014.6906719
Sidana, A. and Aggarwal, H. (2017). Review of web usage of data mining in web mining. Int. J. Adv. Res. Comput. Sci., 8(5), 1-5.
Song, J., Song, T. M., Seo, D. C. and Jin, J. H. (2016). Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents. J. Adolesc. Heal., 59(6), 668-673. https://doi.org/10.1016/j.jadohealth.2016.07.025
Spadon, G., Scabora, L. C., Oliveira, P. H., Araujo, M. V. S., et al. (2017). Behavioral Characterization of Criminality Spread in Cities. Procedia Comput. Sci., 108, 2537-2541. https://doi.org/10.1016/J.PROCS.2017.05.118
Stenton, A. E. (2006). Crime Analysis: An Examination of Crime Prevention and Reduction Strategies. University of Ottawa.
Talib, R., Kashif, M., Ayesha, S. and Fatima, F. (2016). Text Mining: Techniques, Applications and Issues. Int. J. Adv. Comput. Sci. Appl., 7(11), 414-418. https://doi.org/10.14569/ijacsa.2016.071153
Thelwall, M. (2001). A web crawler design for data mining. J. Inf. Sci., 27(5), 319-325. https://doi.org/10.1177/016555150102700503
Tollenaar, N. and van der Heijden, P. G. M. (2009). Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models. Journal of the Royal Statistical Society, Statistics in Society, Series A, 176(2), 565-584. https://doi.org/10.1111/j.1467-985X.2012.01056.x
Tollenaar, N. and Van Der Heijden, P. G. M. (2019). Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes. PLOS ONE, 14(3), e0213245. https://doi.org/10.1371/journal.pone.0213245
Vellani, K. H. (2010). Crime Analysis: for Problem Solving Security Professionals in 25 Small Steps. Available at: https://popcenter.asu.edu/sites/default/files/library/reading/PDFs/crimeanalysis25steps.pdf
Verma, T. and Gaur, D. (2014). Tokenization and Filtering Process in RapidMiner. Int. J. Appl. Inf. Syst., 7(2), 16-18. https://doi.org/10.5120/ijais14-451139
Vishal Gupta, G. S. L. (2009). A Survey of Text Mining Techniques and Applications. J. Emerg. Technol. web Intell., 1(1), 17. https://doi.org/10.4304/jetwi.1.1.60-76
Vishwakarma, R. K. and Shankar, R. (2014). Modeling brain and behavior of a terrorist through fuzzy logic and ontology. IEEE Int. Conf. Ind. Eng. Eng. Manag., 857-861. https://doi.org/10.1109/IEEM.2013.6962533
Yang, X., Sun, N., Zhang, Y. and Kong, D. (2008). General Framework for Text Classification Based on Domain Ontology. Semant. Media Adapt. Pers. 2008. SMAP ‘08. Third Int. Work., pp. 147–152. https://doi.org/10.1109/SMAP.2008.17

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Saldaña M. An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online. J INFORM SYSTEMS ENG. 2020;5(4):em0126. https://doi.org/10.29333/jisem/8492

APA

Saldaña, M. (2020). An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online. Journal of Information Systems Engineering and Management, 5(4), em0126. https://doi.org/10.29333/jisem/8492

AMA

Saldaña M. An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online. J INFORM SYSTEMS ENG. 2020;5(4), em0126. https://doi.org/10.29333/jisem/8492

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Saldaña, Manuel. "An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online". Journal of Information Systems Engineering and Management 2020 5 no. 4 (2020): em0126. https://doi.org/10.29333/jisem/8492

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Saldaña, M. (2020). An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online. Journal of Information Systems Engineering and Management, 5(4), em0126. https://doi.org/10.29333/jisem/8492

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Saldaña, Manuel "An Empirical Study of the Perception of Criminality through Analysis of Newspapers Online". Journal of Information Systems Engineering and Management, vol. 5, no. 4, 2020, em0126. https://doi.org/10.29333/jisem/8492