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International Conference on Design Science Research in Information Systems and Technology

DESRIST 2018: Designing for a Digital and Globalized World pp 300–315Cite as

A Prediction Model of Privacy Control for Online Social Networking Users

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10844))

Abstract

With the growing popularity of social network sites (SNS), organizations have started to leverage them for encouraging both personal and professional data sharing. However, inherent privacy problems in social networks have become a concern for organizations deploying them. So companies have started investing in systems for evaluating employees’ behaviors on SNSs. In evaluating employees’ behaviors on SNSs, this study aims at developing a mechanism for learning users’ behaviors on SNS and predicting their control of privacy on SNS. Privacy prediction is based on the revelation of actual privacy characteristics of users through the analysis of their SNS usage patterns. Using the Design Science research methodology, this study presents the design and instantiation of a prediction model that is trained using survey data and SNS data of graduate students from a prominent Northeastern University in the United States, which is used to generate class labels associated with their privacy control. The prediction model provides a data analytics component for reliable predictions of users’ privacy control using Machine Learning algorithm SVM and a randomized ensemble of decision trees. The results suggest that the prediction model represents a reliable method for predicting privacy control based on user actions on SNS.

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Notes

  1. 1.

    https://clutch.co/agencies/social-media-marketing/resources/social-media-survey-2017.

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Acknowledgements

This research has been funded in part by NSF under grants 1651475, 0916612 and 1227353. Usual disclaimer applies. The authors would also like to thank the reviewers whose comments have greatly improved the paper.

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Authors and Affiliations

  1. University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Rohit Valecha & H. Raghav Rao

  2. State University of New York at Buffalo, Buffalo, NY, 14260, USA

    Rajarshi Chakraborty & Shambhu Upadhyaya

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  1. Rohit Valecha
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  2. Rajarshi Chakraborty
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  3. H. Raghav Rao
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  4. Shambhu Upadhyaya
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Correspondence to Rohit Valecha .

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Editors and Affiliations

  1. School of Information Systems and Technology, Claremont Graduate University, Claremont, California, USA

    Samir Chatterjee

  2. Muma College of Business, University of South Florida, Tampa, Florida, USA

    Kaushik Dutta

  3. Department of Management Studies, IIT Madras, Chennai, Tamil Nadu, India

    Rangaraja P. Sundarraj

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Valecha, R., Chakraborty, R., Rao, H.R., Upadhyaya, S. (2018). A Prediction Model of Privacy Control for Online Social Networking Users. In: Chatterjee, S., Dutta, K., Sundarraj, R. (eds) Designing for a Digital and Globalized World. DESRIST 2018. Lecture Notes in Computer Science(), vol 10844. Springer, Cham. https://doi.org/10.1007/978-3-319-91800-6_20

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  • DOI: https://doi.org/10.1007/978-3-319-91800-6_20

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