Many social media sites permit users to delete, edit, anonymize, or otherwise modify past posts. These mechanisms enable users to protect their privacy, but also to essentially change the past. We investigate perceptions of the necessity and acceptability of these mechanisms. Drawing on boundary-regulation theories of privacy, we first identify how users who reshared or responded to a post could be impacted by its retrospective modification. These mechanisms can cause boundary turbulence by recontextualizing past content and limiting accountability. In contrast, not permitting modification can lessen privacy and perpetuate harms of regrettable content. To understand how users perceive these mechanisms, we conducted 15 semi-structured interviews. Participants deemed retrospective modification crucial for fixing past mistakes. Nonetheless, they worried about the potential for deception through selective changes or removal. Participants were aware retrospective modification impacts others, yet felt these impacts could be minimized through context-aware usage of markers and proactive notifications.
Moving Beyond Set-It-And-Forget-It Privacy Settings on Social Media
When users post on social media, they protect their privacy by choosing an access control setting that is rarely revisited. Changes in users' lives and relationships, as well as social media platforms themselves, can cause mismatches between a post's active privacy setting and the desired setting. The importance of managing this setting combined with the high volume of potential friend-post pairs needing evaluation necessitate a semi-automated approach. We attack this problem through a combination of a user study and the development of automated inference of potentially mismatched privacy settings. A total of 78 Facebook users reevaluated the privacy settings for five of their Facebook posts, also indicating whether a selection of friends should be able to access each post. They also explained their decision. With this user data, we designed a classifier to identify posts with currently incorrect sharing settings. This classifier shows a 317% improvement over a baseline classifier based on friend interaction. We also find that many of the most useful features can be collected without user intervention, and we identify directions for improving the classifier's accuracy.
- Publication Date:
- NSF-PAR ID:
- 10148264
- Journal Name:
- Proceedings of the 26th ACM Conference on Computer and Communications Security (CCS)
- Page Range or eLocation-ID:
- 991 to 1008
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3319535.3354202
