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Azzopardi, L., Nicol, E., Briggs, J., Moncus, W., Schafer, B., Nash, C., Duheric, M., 2025.

Assessing Risks in Online Information Sharing

Output Type:Conference paper
Venue:Melbourne, Victoria, Australia
Publisher:Association for Computing Machinery (ACM)
Dates:24/3/2025 - 28/3/2025
URL:chiir2025.github.io/index.html

The volume of personal information, accessible online about individuals is unprecedented. Such information may be pieced together by others, to create a more detailed picture of a person, exposing them to potential risks and harms, such as employment loss, unwanted attention or fraud. In this context, relevance is contextual, situational and dependent, based on the risk it poses to the subject of the information. In this paper, we explore this notion of relevance with the following research questions: How well can individuals identify and judge risks associated with another person's online personal information? To what extent does this change individuals' awareness of their own information-sharing practices? In a user study, 243 participants were tasked with browsing fabricated online profiles to identify potential "risky" posts. On average, 72.2% of participants identified at least one risky post. However, only 23.7% identified dependent posts that cumulatively could substantially increase the risk of identity theft or reputational harm. Further, participants reported greater awareness of potential risks that could arise from their own, and/or their friends' information sharing practices. Our findings suggest that when "relevance" is dependent on combining separate pieces of information to reveal "risk", participants struggle with performing the identification. Moreover, our study highlights that when participants perform tasks that feature personal information, it can lead to positive and negative experiences; changing their perceptions and increasing awareness about their own information behaviours while also raising concerns around their routine online practices.