IJBTT

NoSQL Database Design for SNS Profiling in Criminal Investigations

	International Journal of Engineering Trends and Technology (IJETT)
	© 2020 by IJETT Journal
	Volume-68 Issue-11
	Year of Publication : 2020
	Authors : Jiyeon Kim, Minsun Shim, Seyoung Jin, Seung Hoon Lee, In Soo Lee, Myuhng-Joo Kim
	DOI :  10.14445/22315381/IJETT-V68I11P214

Citation 

MLA Style: Jiyeon Kim, Minsun Shim, Seyoung Jin, Seung Hoon Lee, In Soo Lee, Myuhng-Joo Kim  "NoSQL Database Design for SNS Profiling in Criminal Investigations" International Journal of Engineering Trends and Technology 68.11(2020):105-112. 

APA Style:Jiyeon Kim, Minsun Shim, Seyoung Jin, Seung Hoon Lee, In Soo Lee, Myuhng-Joo Kim. NoSQL Database Design for SNS Profiling in Criminal Investigations  International Journal of Engineering Trends and Technology, 68(11),105-112.

Abstract
Social Network Service (SNS) is an online platform for users to communicate and share their interests. As the number of SNS users increases, profiling techniques that collect and analyze user information from SNS have emerged. SNS profiling enables creating personal information of the users and analyzing their interests through their posts, comments, and page likes. SNS profiling has been mainly used as a marketing tool to recommend products through analysis of customer interests. However, SNS profiling has recently been used for various other purposes. In this work, we focus on the use of SNS profiling for criminal investigations. We design a database to store information collected from SNS and propose a model for event profiling. In the database, we create tables of people, events, SNS users, and posts based on NoSQL, a non-relational database, and build a database using DynamoDB. In addition, we conducted a case study for profiling events based on the proposed model.

Reference

[1] Smart Insights website. [Online]. Available: https://www.smartinsights.com/social-media-marketing/ social-media-strategy/new-global-social-media-research/ (2020)
[2] Smart Insights website. [Online]. Available: https://www.smartinsights.com/internet-marketing-statistics/happens-online-60-seconds/(2020)
[3] J. Dam and M. Velden, Online profiling and clustering of Facebook users, Decision Support Systems, 70 (2015) 60-72.
[4] A. Antoniou, I. Lykourentzou, J. Rompa, E. Tobias, G. Lepouras, C. Vassilakis, and Y. Naudet. User profiling: Towards a Facebook game that reveals cognitive style. in Proc. International Conference on Games and Learning Alliance : 2013. 349-353.
[5] Shen, B., and Bissell, K., Social media, social me: A content analysis of beauty companies` use of Facebook in marketing and branding, Journal of Promotion Management, 19(5) (2013) 629-651.
[6] Homeland Security Digital Library website. [Online]. Available: https://www.hsdl.org/c/us-national-intelligence-an-overview-2011/ (2012)
[7] Shu, K., Wang, S., Tang, J., Zafarani, R. and Liu, H., User identity linkage across online social networks: A review, ACM Sigkdd Explorations Newsletter, 18(2), (2017) 5-17.
[8] Malhotra, A., Totti, L., Meira Jr, W., Kumaraguru, P. and Almeida, V, Studying user footprints in different online social networks, in Proc. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining : 2012. 1065-1070.
[9] Farnadi, G., Tang, J., De Cock, M. and Moens, M. F, User profiling through deep multimodal fusion, in Proc. the Eleventh ACM International Conference Web Search and Data Mining, pp. 171-179, 2018.
[10] Vasanthakumar, G. U., Sunithamma, K., Shenoy, P. D. and Venugopal, K. R., An overview on user profiling in online social networks, International Journal of Applied Information System, 11(8) (2017) 25-42.
[11] Myo-Seop Sim and Heui-Seok Lim, Predicting User Profile based on user behaviors, Journal of the Korea Convergence Society, 11(7) (2020) 1-7.
[12] Tang, J., Yao, L., Zhang, D. and Zhang, J, A combination approach to web user profiling, ACM Transactions on Knowledge Discovery from Data, 5(1) , 2010 1-44.
[13] Pellet, H., Shiaeles, S. and Stavrou, S, Localising social network users and profiling their movement, Computers and Security, 81 (2019) 49-57.

Keywords
SNS profiling, user profiling, event profiling, social network service, open-source intelligence, criminal investigations