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1. Your Photo is so Funny that I don’t Mind Violating Your Privacy by Sharing it: Effects of Individual Humor Styles on Online Photo-sharing Behaviors

   Hasan, Rakibul ; Bertenthal, Bennett I. ; Hugenberg, Kurt ; Kapadia, Apu ( May 2021 , In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '21))

   null (Ed.)

   We investigate how people’s ‘humor style’ relates to their online photo-sharing behaviors and reactions to ‘privacy primes’. In an online experiment, we queried 437 participants about their humor style, likelihood to share photo-memes, and history of sharing others’ photos. In two treatment conditions, participants were either primed to imagine themselves as the photo-subjects or to consider the photo-subjects’ privacy before sharing memes. We found that participants who frequently use aggressive and self-deprecating humor were more likely to violate others’ privacy by sharing photos. We also replicated the interventions’ paradoxical effects – increasing sharing likelihood – as reported in earlier work and identified the subgroups that demonstrated this behavior through interaction analyses. When primed to consider the subjects’ privacy, only humor deniers (participants who use humor infrequently) demonstrated increased sharing. In contrast, when imagining themselves as the photo-subjects, humor deniers, unlike other participants, did not increase the sharing of photos. 

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2. Modeling User Characteristics Associated with Interdependent Privacy Perceptions on Social Media

   https://doi.org/10.1145/3577014  

   Amon, Mary Jean ; Necaise, Aaron ; Kartvelishvili, Nika ; Williams, Aneka ; Solihin, Yan ; Kapadia, Apu ( January 2023 , ACM Transactions on Computer-Human Interaction)

   ‘Interdependent’ privacy violations occur when users share private photos and information about other people in social media without permission. This research investigated user characteristics associated with interdependent privacy perceptions, by asking social media users to rate photo-based memes depicting strangers on the degree to which they were too private to share. Users also completed questionnaires measuring social media usage and personality. Separate groups rated the memes on shareability, valence, and entertainment value. Users were less likely to share memes that were rated as private, except when the meme was entertaining or when users exhibited dark triad characteristics. Users with dark triad characteristics demonstrated a heightened awareness of interdependent privacy and increased sharing of others’ photos. A model is introduced that highlights user types and characteristics that correspond to different privacy preferences: privacy preservers, ignorers, and violators. We discuss how interventions to support interdependent privacy must effectively influence diverse users. 

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3. Learning and Preserving Relationship Privacy in Photo Sharing

   https://doi.org/10.1109/BDCAT56447.2022.00029  

   Liu, Jialin ; Li, Lin ; Li, Na ( December 2022 , EEE/ACM International Conference on Big Data Computing, Applications and Technologies)

   In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss. 

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4. Sharenting and children’s privacy: Parenting style, practices, and perspectives on sharing young children’s photos on social media

   https://doi.org/10.1145/3512963  

   Amon, M. J. ; Kartvelishvilli, N. ; Bertenthal, B. ; Hugenberg, K. ; Kapadia, A. ( January 2022 , Computer supported cooperative work)

   Parents posting photos and other information about children on social media is increasingly common and a recent source of controversy. We investigated characteristics that predict parental sharing behavior by collecting information from 493 parents of young children in the United States on self-reported demographics, social media activity, parenting styles, children’s social media engagement, and parental sharing attitudes and behaviors. Our findings indicate that most social media active parents share photos of their children online and feel comfortable doing so without their child’s permission. The strongest predictor of parental sharing frequency was general social media posting frequency, suggesting that participants do not strongly differentiate between “regular” photo-sharing activities and parental sharing. Predictors of parental sharing frequency include greater social media engagement, larger social networks with norms encouraging parental sharing, more permissive and confident parenting styles, and greater social media engagement by their children. Contrasting previous research that often highlights benefits of parental sharing, our findings point to a number of risky online behaviors associated with parental sharing not previously uncovered. Implications for children’s privacy and early social media exposure are discussed, including future directions for influencing parental sharing attitudes and behaviors. 

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5. Decaying Photos for Enhanced Privacy: User Perceptions Towards Temporal Redactions and 'Trusted' Platforms

   https://doi.org/10.1145/3555538  

   Pias, Sabid Bin ; Ahmad, Imtiaz ; Akter, Taslima ; Kapadia, Apu ; Lee, Adam J. ( November 2022 , Proceedings of the ACM on Human-Computer Interaction)

   With the rising popularity of photo sharing in online social media, interpersonal privacy violations, where one person violates the privacy of another, have become an increasing concern. Although applying image obfuscations can be a useful tool for improving privacy when sharing photos, prior studies have found these obfuscation techniques adversely affect viewers' satisfaction. On the other hand, ephemeral photos, popularized by apps such as Snapchat, allow viewers to see the entire photo, which then disappears shortly thereafter to protect privacy. However, people often use workarounds to save these photos before deletion. In this work, we study people's sharing preferences with two proposed 'temporal redactions', which combines ephemerality with redactions to allow viewers to see the entire image, yet make these images safe for longer storage through a gradual or delayed application of redaction on the sensitive portions of the photo. We conducted an online experiment (N=385) to study people's sharing behaviors in different contexts and under different levels of assurance provided by the viewer's platform (e.g., guaranteeing temporal redactions are applied through the use of 'trusted hardware'). Our findings suggest that the proposed temporal redaction mechanisms are often preferred over existing methods. On the other hand, more efforts are needed to convey the benefits of trusted hardware to users, as no significant differences were observed in attitudes towards 'trusted hardware' on viewers' devices. 

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