Your browser does not support the PubReader view.
Go to this page to see a list of supporting browsers.
Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 50, No. 1, pp.197-214
ISSN: 1226-1874 (Print)
Print publication date 28 Feb 2021
Received 27 Jul 2020 Revised 07 Oct 2020 Accepted 21 Oct 2020
DOI: https://doi.org/10.17287/kmr.2021.50.1.197

스팸 필터링 앱 사용자의 이탈 요인 연구: 이탈자와 지속 사용자 비교 및 앱 로그 빅데이터 분석을 중심으로

Ae Ri Lee ; Chanhee Kwak
(First Author) Dept. of Business Administration, Sangmyung University sharon@smu.ac.kr
(Corresponding Author) Dept. of Industrial Data Science, Kangnam University chk@kangnam.ac.kr
Investigating the Factors Influencing User Churning Behavior in Spam Filtering Apps: A Comparison between Churners and Users and Big Data Analysis of App Logs


Copyright 2011 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, various spam calls and messages targeting mobile phone users have been continuously increasing. As the types of spam are becoming diversified with increasing frequency, it not only causes considerable inconvenience to mobile users, but also becomes a risk factor in information security and financial accidents. Telecommunication companies have developed and distributed spam filtering apps, and mobile users are increasingly interested in spam blocking and spam information sharing services. Nevertheless, there have been behaviors that do not use or stop using spam filtering apps. Therefore, it is important for companies and organizations, which need to increase the usage rate of spam filtering apps, to diagnose the use status of spam filtering apps and to analyze how customer churn occurs. This study compares the usage patterns of spam filtering apps between users and churners, and identifies the characteristic behaviors of churners. In particular, by analyzing the big data of app usage log, we derive the factors that affect the increase in churn rate. With the results of this study, it contributes to establishing a safer mobile phone environment by preventing customers from churning and activating the use of spam filtering functions.

Keywords:

Mobile Spam Filtering App, Big Data Analysis, Customer Churn Factor

References

  • Ahn, H. Y., Cho, W. Z., and Lee, J. W.(2015), “Implementation of A Mobile Application for Spam SMS Filtering Using Set-Based POI Search Algorithm,” Journal of Digital Contents Society, 16(5), pp.815-822. [https://doi.org/10.9728/dcs.2015.16.5.815]
  • Almeida, T. A., Hidalgo, J. M. G., and Yamakami, A.(2011), “Contributions to the study of SMS spam filtering: new collection and results,” In Proceedings of the 11th ACM Symposium on Document Engineering, pp. 259-262. [https://doi.org/10.1145/2034691.2034742]
  • Batista, G. E., Prati, R. C., and Monard, M. C. (2004), “A study of the behavior of several methods for balancing machine learning training data,” ACM SIGKDD Explorations Newsletter, 6(1), pp.20-29. [https://doi.org/10.1145/1007730.1007735]
  • Bin, Z., Gang, Z., Yunbo, F., Xiaolu, Z., Weiqiang, J., Jing, D., and Jiafeng, G.(2016), “Behavior analysis based SMS spammer detection in mobile communication networks,” In 2016 IEEE First International Conference on Data Science in Cyberspace (DSC), pp.538-543. [https://doi.org/10.1109/DSC.2016.48]
  • Byun, S. M., and Kim, J.(2014), “Spam Message Filtering System using Message Digest Algorithm,” In Proceedings of KIIT Conference, pp.120-123.
  • DigitalTimes, “Over 130 million spam reports in the last 5 years….010 Outgoing rate surge,” 2019, Available at http://www.dt.co.kr/contents.html?article_no=2019092902109931032002, .
  • Farquad, M. A. H., and Bose, I.(2012), “Preprocessing unbalanced data using support vector machine,” Decision Support Systems, 53(1), pp.226-233. [https://doi.org/10.1016/j.dss.2012.01.016]
  • Hong, S. S(2019), “A Comparative Study on the Phishing Fraud Prevention,” The Police Science Journal, 14(1), pp.101-130. [https://doi.org/10.16961/polips.2019.14.1.101]
  • Hosmer, D., and Lemeshow, S.(1989), Applied Logistic Regression, Wiley & Sons, New York.
  • Jang, Y. J.(2015), “Big Data, Business Analytics, and IoT: The Opportunities and Challenges for Business,” The Journal of Information Systems, 24(4), pp.139-152. [https://doi.org/10.5859/KAIS.2015.24.4.139]
  • Jiang, M., Cui, P., and Faloutsos, C.(2016), “Suspicious behavior detection: Current trends and future directions,” IEEE Intelligent Systems, 31 (1), pp.31-39. [https://doi.org/10.1109/MIS.2016.5]
  • Joe, I. W., and Shim, H. T.(2009), “A SVM-based spam filtering system for short message service (SMS),” The Journal of Korean Institute of Communications and Information Sciences, 34(9B), pp.908-913.
  • Jung, S. K., Seo, M. H. I., Park, I. S., and Park, H. S.(2019), “A Study on User Experience Design of Spam Block/Sharing Service”, In Proceedings of The HCI Society of Korea, pp.1018-1021.
  • Kim, K. H., Kim, G. J., and Lee, S. J.(2019), “Media Literacy Components and Generation Gap in the Mobile Environment,” Korean Journal of Broadcasting, 33(4), pp.55-36.
  • Kim, S. S., and Kim, Y. J.(2019), “An Empirical Study on Users Intention to Use Insurtech Digital Insurance Platform Service,” Korean Management Review, 48(4), pp.997-1043. [https://doi.org/10.17287/kmr.2019.48.4.997]
  • Korea Communications Commission(2019), Voice spam, business operator transmission volume increases, user reception decreases, Korea Communications Commission.
  • Lee, H. Y., and Kang, S. S.(2018), “SMS text messages filtering using word embedding and deep learning techniques,” Smart Media Journal, 7(4), pp.24-29.
  • Lee, S. J., and Choi, D. J.(2011), “Personalized mobile junk message filtering system,” The Journal of the Korea Contents Association, 11(12), pp.122-135. [https://doi.org/10.5392/JKCA.2011.11.12.122]
  • Lee, S. Y., Kang, H. S., and Moon, J. S.(2014), “A study on smishing block of android platform environment,” Journal of The Korea Institute of Information Security & Cryptology, 24 (5), pp.975-985. [https://doi.org/10.13089/JKIISC.2014.24.5.975]
  • Ng, A. Y.(2004), “Feature selection, L1 vs. L2 regularization, and rotational invariance,” In Proceedings of the twenty-first international conference on Machine learning. [https://doi.org/10.1145/1015330.1015435]
  • Oh, J. H., Hong, J. Y., and Baek, J. G.(2019), “Oversampling method using outlier detectable generative adversarial network,” Expert Systems with Applications, 133, pp.1-8. [https://doi.org/10.1016/j.eswa.2019.05.006]
  • Osho, O., Ogunleke, O. Y., and Falaye, A. A.(2014), “Frameworks for mitigating identity theft and spamming through bulk messaging,” In 2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), pp.1-6. [https://doi.org/10.1109/ICASTECH.2014.7068119]
  • Park, I. W., and Park, D. W.(2013), “A study of intrusion security research and smishing hacking attack on a smartphone,” Journal of the Korea Institute of Information and Communication Engineering, 17(11), pp. 2588-2594. [https://doi.org/10.6109/jkiice.2013.17.11.2588]
  • Prusa, J., Khoshgoftaar, T. M., Dittman, D. J., and Napolitano, A.(2015), “Using random undersampling to alleviate class imbalance on tweet sentiment data,” In 2015 IEEE International Conference on Information Reuse and Integration, pp.197-202. [https://doi.org/10.1109/IRI.2015.39]
  • Roy, P. K., Singh, J. P., and Banerjee, S.(2020), “Deep learning to filter SMS spam,” Future Generation Computer Systems, 102, pp.524- 533. [https://doi.org/10.1016/j.future.2019.09.001]
  • Sethi, P., Bhandari, V., and Kohli, B.(2017), “SMS spam detection and comparison of various machine learning algorithms,” In 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN), pp.28-31. [https://doi.org/10.1109/IC3TSN.2017.8284445]
  • Wang, C., Zhang, Y., Chen, X., Liu, Z., Shi, L., Chen, G., and Lu, W.(2010), “A behavior-based SMS antispam system,” IBM Journal of Research and Development, 54(6), pp.1-16. [https://doi.org/10.1147/JRD.2010.2066050]
  • White, H.(1980), “A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity,” Econometrica: Journal of the Econometric Society, 48(4), pp.817-838. [https://doi.org/10.2307/1912934]
  • Yeon, B. H., Kang, W. Y., and Choi, S. J.(2019), “A Study on the Configuration of Pre-install Applications on Smartphone for Customer Needs,” Journal of Broadcast Engineering, 24(1), pp.105-117.

∙ The author Ae Ri Lee is an assistant professor in the Department of Business Administration at Sangmyung University. She worked at Korea Telecom as a senior manager in Business Planning and R&D Division. She received her Ph.D. in Information Systems from Yonsei University and her MBA in Technology Management from KAIST. Her research interests include digital transformation, business intelligence, and information security & privacy. She has published papers in Information & Management, Computers in Human Behavior, Internet Research, Behaviour & IT, and Journal of Global Information Management.

∙ The author Chanhee Kwak is an assistant professor in the Department of Industrial Data Science at Kangnam University. He received Ph.D. degree in management engineering from Korea Advanced Institute of Science and Technology. His research interests include data analytics, privacy and information systems, and digital transformation. His research has been published in International Journal of Information Management and Journal of Knowledge Management.