Predicting Privacy Behavior on Online Social Networks
| Autor: | Cailing Dong, Hongxia Jin, Bart Knijnenburg |
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| Rok vydání: | 2021 |
| Zdroj: | Proceedings of the International AAAI Conference on Web and Social Media. 9:91-100 |
| ISSN: | 2334-0770 2162-3449 |
| Popis: | Online Social Networks (OSNs) have come to play an increasingly important role in our social lives, and their inherent privacy problems have become a major concern for users. Can we assist consumers in their privacy decision-making practices, for example by predicting their preferences and giving them personalized advice? In order to accomplish this, we would need to study the factors that affect users’ privacy decision-making practices. In this paper, we intend to comprehensively investigate these factors in light of two common OSN scenarios: the case where other users request access to the user’s information, and the case where the user shares this information voluntarily. Using a real-life dataset from Google+ and three location-sharing datasets, we identify behavioral analogs to psychological variables that are known to affect users’ disclosure behavior: the trustworthiness of the requester/information audience, the sharing tendency of the receiver/information holder, the sensitivity of the requested/shared information, the appropriateness of the request/sharing activity, as well as some contextual information. We also explore how these factors work to affect the privacy decision making. Based on these factors we build a privacy decisionmaking prediction model that can be used to give users personalized advice regarding their privacy decisionmaking practices. |
| Databáze: | OpenAIRE |
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