DETERMINATION OF POTENTIAL CRIMINALS IN SOCIAL NETWORK

Yıl 2017, Cilt: 30 Sayı: 1, 121 - 131, 14.03.2017

Eyüp Burak Ceyhan Şeref Sağıroğlu Ramazan Cesur Ayten Kermen

Öz

This paper proposes a new approach to determine potential criminals by performing content analysis on Tweets. The analysis is done with the help of machine learning technologies and big data analysis. In this study, we have utilized from the MLP algorithm. A dataset consisting of 384 words are used to make the classification process. Dataset consist of two classes that are organized crime and cyber-crimes. In the analysis process, date, time, location values of sent twitter sharings are also used. Criminals can be detected with a success rate of 71,61%. Also, the developed system identifies potential criminals who commit an offence of organized crime and cybercrime with the world's most widely used social media. User can use the system to get accurate results for scanning potential criminals with analyzing their sharings or scanning keywords to reach potential criminals. In addition to this property, user can get results that are more accurate with narrowing the content screening with location and date information. It is thought that the proposed system might help to find criminals and the security forces can easily detect them by the developed software.

Anahtar Kelimeler

Twitter analysis, machine learning, cyber-crimes, social network, crime analysis

Kaynakça

• Cinar, B., “Sosyal Medyanın Örgütlü Suç İşlemede Rolü”, JOBEPS: International Journal of Business, Economics and Political Science, 1(2): 79-102, (2012).
• Acar, A. and Deguchi, A., “Culture and Social Media Usage: Analysis of Japanese Twitter Users”, International Journal of Electronic Commerce Studies, 4(1): 21-32, (2013).
• Signorini, A., Segre, A. M. and Polgreen, P. M., “The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic” PLoS ONE, 6(5): 1-10, (2011).
• Internet: “TUIK Statistics”, http://www.tuik.gov.tr/PreHaberBultenleri.do?id=16198, Access Date: 17.05.2016.
• Letierce, J., Passant, A., Breslin, J. and Decker, S., “Understanding How Twitter is Used to Spread Scientific Messages”, Web Science Conference (WebSci10), Raleigh, NC, USA, 1-8, (2010).
• Özdemir, A., “Adli Bilişim Araçları”, 1st International Symposium on Digital Forensics and Security, Elazig, Turkey, 1-4, (2013).
• Crook, E., “The Twitter Landscape”, A Brand watch social insights report, (2012).
• Internet: “Social Media Active Users by Network [INFOGRAPH]”, http://www.thesocialmediahat.com/active-users, Access Date: 17.05.2016.
• Gupta, M., Zhao, P. and Han, J., “Evaluating Event Credibility on Twitter”, 12. SIAM International Conference on Data Mining, California, USA, 153-164, (2012).
• Acar, A. and Muraki, Y., “Twitter for Crisis Communication: Lessons Learned from Japan's Tsunami Disaster”, International Journal of Web Based Communities, 7(3): 392-402, (2011).
• Sakaki, T., Okazaki, M. and Matsuo, Y., “Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors” in Proceedings of the 19th International Conference on World Wide Web, New York, USA, 851-860, (2010).
• Takahashi, B., Tandoc, E. C. and Carmichael, C., “Communicating on Twitter during a Disaster: An Analysis of Tweets during Typhoon Haiyan in the Philippines”, Computers in Human Behavior, 50: 392-398, (2015).
• Bozdag, E., Gao, Q., Houben, G. and Warnier, M., “Does Offline Political Segregation Affect the Filter Bubble? An Empirical Analysis of Information Diversity for Dutch and Turkish Twitter Users”, Computers in Human Behavior, 41: 405-415, (2014).
• Adams, A. and McCorkindale, T., “Dialogue and Transparency: A Content Analysis of How the 2012 Presidential Candidates Used Twitter”, Public Relations Review, 39(4): 357-359, (2013).
• Yu, Y. and Wang, X., “World Cup 2014 in the Twitter World: A Big Data Analysis of Sentiments in U.S. Sports Fans’ Tweets”, Computers in Human Behavior, 48: 392-400, (2015).
• Kolozali, S., Barthet, M. and Sandler, M., “Knowledge Management on the Semantic Web: A Comparison of Neuro-Fuzzy and Multi-Layer Perceptron Methods for Automatic Music Tagging”, 9th International Symposium on Computer Music Modelling and Retrieval (CMMR 2012), London, England, 220-231, (2012).
• Conover, M., Goncalves, B., Ratkiewicz, J., Flammini, A. and Menczer, F., “Predicting the Political Alignment of Twitter Users”, IEEE Third Inernational Conference on Social Computing (SocialCom), Massachusetts, USA, 192-199, (2011).
• Gerber, M.S., “Predicting crime using Twitter and kernel density estimation”, Decision Support Systems, 61: 115-125, (2014).
• Chen, X., Cho, Y., Jang, S.Y., “Crime Prediction Using Twitter Sentiment and Weather”, Systems and Information Engineering Design Symposium, Charlottesville, USA, 63-68, (2015).