Online Self-disclosure: From Users' Regrets to Instructional Awareness

Abstract

Unlike the offline world, the online world is devoid of well- evolved norms of interaction which guide socialization and self-disclosure. Therefore, it is difficult for members of online communities like Social Network Sites (SNSs) to control the scope of their actions and predict others’ reactions to them. Consequently users might not always antici- pate the consequences of their online activities and often engage in ac- tions they later regret. Regrettable and negative self-disclosure experi- ences can be considered as rich sources of privacy heuristics and a valu- able input for the development of privacy awareness mechanisms. In this work, we introduce a Privacy Heuristics Derivation Method (PHeDer) to encode regrettable self-disclosure experiences into privacy best practices. Since information about the impact and the frequency of unwanted inci- dents (such as job loss, identity theft or bad image) can be used to raise users’ awareness, this method (and its conceptual model) puts special focus on the risks of online self-disclosure. At the end of this work, we provide assessment on how the outcome of the method can be used in the context of an adaptive awareness system for generating tailored feedback and support.

Publication
Holzinger A., Kieseberg P., Tjoa A., Weippl E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2017. Lecture Notes in Computer Science, vol. 10410, pp. 83–102, https://doi.org/10.1007/978-3-319-66808-6_7

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