Date of this Version
Chaudhari, A., Joshi, R. R., Mulay, P., Kotecha, K., & Kulkarni, P. (2019). Bibliometric Survey on Incremental Clustering Algorithms. Library Philosophy and Practice, 1-23.
We are witness to see exponential growth of the social media network since the year 2002. Leading social media networking sites used by people are Twitter, Snapchats, Facebook, Google, and Instagram, etc. The latest global digital report (Chaffey and Ellis-Chadwick 2019) states that there exist more than 800 million current online social media users, and the number is still exploding day by day. Users share their day to day activities such as their photos and locations etc. on social media platforms. This information gets consumed by third party users, like marketing companies, researchers, and government firms. Depending upon the purpose, there is a possibility of misuse of the user's personal & sensitive information. Users' sensitive information breaches can further utilized for building a personal profile of individual users and also lead to the unlawful tracing of the individual user, which is a major privacy threat. Thus it is essential to first anonymize users' information before sharing it with any of the third parties. Anonymization helps to prevent exposing sensitive information to the third party and avoids its misuse too. But anonymization leads to information loss, which indirectly affects the utility of data; hence, it is necessary to balance between data privacy and utility of data.
This research paper presents a bibliometric analysis of social media privacy and provides the exact scope for future research. The research objective is to analyze different research parameters and get insights into privacy in Social Media Network (OSN). The research paper provides visualization of the big picture of research carried on the privacy of the social media network from the year 2010 to 2019 (covers the span of 19 years). Research data is taken from different online sources such as Google Scholar, Scopus, and Research-gate. Result analysis has been carried out using open source tools such as Gephi and GPS Visualizer.
Maximum publications of privacy of the social media network are from articles and conferences affiliated to the Chinese Academy of Science, followed by the Massachusetts Institute of Technology. Social networking is a frequently used keyword by the researchers in the privacy of the online social media network. Major Contribution in this subject area is by the computer science research community, and the least research contribution is from art and science. This study will clearly give an understanding of contributions in the privacy of social media network by different organizations, types of contributions, more cited papers, Authors contributing more in this area, the number of patents in the area, and overall work done in the area of privacy of social media network.