FemHealth apps have rapidly developed, offering innovative opportunities to track users’ menstrual cycles, fertility, pregnancy, and other aspects of sexual and reproductive health. However, such apps collect a significant amount of sensitive user health data, posing privacy risks to users. In this paper, we conducted 14 in-depth semistructured interviews with current and past users of FemHealth apps in the US to examine their privacy experiences and concerns. We found that participants were concerned about a wider range of risks than was found in prior user research about FemTech, including criminalization related to abortion or contraceptive access; emotional distress related to social stigma; third-party data sharing; and targeted advertising based on processing sensitive health data. Some participants acknowledged that FemHealth apps posed privacy risks and potential harms to users in general but were not necessarily concerned about their own privacy due to privilege (e.g., living in a state with strong reproductive health rights). However, all participants agreed that user privacy and data protection in FemHealth apps should be considered a fundamental right, not subject to legal discourse in specific locales. Most participants felt unsure about the effectiveness of existing data protection regulations and their interplay with anti-abortion laws. Participants suggested several ways to mitigate privacy risks, including disclosures and controls, back-end technical protections, behavioral strategies, and policy improvements. We provide recommendations for extending practical and policy-based privacy protections of sexual and reproductive health data collected by FemHealth apps.

Toxicity detection algorithms , originally designed for reactive content moderation systems, are being deployed into proactive end-user interventions to moderate content. Yet, there has been little critique on the use of these algorithms within this moderation paradigm. We conducted design workshops with four stakeholder groups , asking participants to embed a toxicity detection algorithm into an imagined mobile phone keyboard. This allowed us to critically explore how such algorithms could be used to proactively reduce the sending of toxic content. We found contextual factors such as platform culture and affordances, and scales of abuse, impacting on perceptions of toxicity and effectiveness of the system. We identify different types of end-users across a continuum of intention to send toxic messages, from unaware users, to those that are determined and organised. Finally, we highlight the potential for certain end-user groups to misuse these systems to validate their attacks, to gamify hate, and to manipulate algorithmic models to exacerbate harm.

Community norms are important in regulating online identity-based communities as they help shape both online and offline discourse. Yet, not all expressions of identity are treated equally as acceptable forms of speech and expression of identity differ among communities. We used mixed-methods to understand how implicit norms within a set of non-binary communities are reinforced and shaped through influence within these communities. We analyzed approximately 2 million Reddit posts and comments to measure the effect of scores, replies, and self-disclosures, on user editing behaviors, which we use as means to observe norm regulation. We find self-disclosures and the number of replies a post receives is positively associated with editing behaviors, while the influence of scores on the likelihood of a message being edited is highly dependent on whether the message is a post or comment. Our qualitative analysis of posts, comments, and threads finds community norms are created, contested, and reinforced through the interactions between community and individual-level understanding of what it means to be non-binary. We propose a model for implicit norms as governance in identity-based communities, and discuss how platform designers can better use implicit norms to support governance in identity-based communities.

Large Language Models (LLMs) are widely used in conversational agents (CAs) due to their ability to generate coherent and human-like text. However, their deployment raises significant privacy concerns, as users often share sensitive data in prompts. This data can be used to train the underlying LLM, introducing memorisation risks and challenging users' right to be forgotten. While these issues have been explored from a technical standpoint, little is known about how users perceive and navigate privacy issues in their day-to-day use of LLM-based CAs. To address this research gap, we conducted a survey of UK-based CA users (n=211) that focused on their privacy behaviours, self-disclosure boundaries, concerns, and awareness of LLM-specific privacy issues. We found that engagement with protective behaviours was low overall, and that many participants held inaccurate beliefs about the effects of deleting data and opting out of model training. Although participants were generally reluctant to share sensitive information during interactions, we identified several challenges to limiting self-disclosure in practice, such as balancing privacy with app utility. Lastly, we observed a nuanced relationship between privacy awareness and concern, and identified significant demographic effects. We propose design avenues for privacy-supportive tools, and discuss the implications of our work for regulation and governance.

Mobile apps which support women’s health have developed rapidly alongside the increasing de-stigmatisation of female reproductive wellbeing. However, the ubiquity of these apps has advanced the practice of intimate surveillance and the commodification of sensitive user data. While the overturning of Roe v. Wade has prompted reflection on the privacy and safety implications of female mobile health (mHealth) apps, the privacy practices of these apps have yet to be thoroughly examined in a post-Roe world. We investigated the privacy practices of 20 popular female mHealth apps, combining a thematic analysis of Data safety sections and privacy policies with a privacy-focused usability inspection. Our findings revealed problematic practices, including inconsistencies across privacy policy content and privacy-related app features, flawed consent and data deletion mechanisms, and covert gathering of sensitive data. We present recommendations for improving privacy practices, and call for a dedicated focus not only on user privacy, but also safety.

People typically enhance their online personas by sharing favourable personal information. Nevertheless, sharing of unfavourable information about oneself still occurs and is essential in some online contexts (e.g. allowing negative reviews). It remains unclear why people reveal potentially damaging information. We conducted an online experiment (N=462) to explore the effects of feedback properties and individual characteristics on online information sharing in two contexts (social and socioeconomic) where personal ratings are essential. We allowed users to conceal their personal rating if it dropped below a threshold. The context was the primary determinant of the threshold users chose. Control availability and feedback content triggered additional considerations and caused some users to change their (non)disclosure choices. However, many users relied on their priors (experience, assumptions) rather than on new information. Our findings show how people may fail to identify the impact of nondisclosure, which may signal undesirable information to others. These findings challenge the reliance on holding users solely accountable for their ‘informedness’ vis-à-vis disclosure of their personal information.

Misinformation has become a regular occurrence in our lives with many different approaches being sought to address it. One effective way to combat misinformation is for trusted individuals (e.g., family members) to challenge the misinformed person. However, less is known about how these conversations between trusted individuals occur, and how they may impact on relationships. We look to address this gap by conducting semi-structured interviews with family members in the UK who have experienced misinformation within their family networks. We identify several barriers individuals face when challenging misinformed family members, such as the misinformed person’s personality and the extent that pre-conceptions influence beliefs. We also find individuals developing strategies to overcome these barriers, and to cope with difficulties that arise through these conversations. Despite technology being the main driver for misinformation spread, we find it has limitations when used to facilitate or mediate conversations for challenging misinformation between family members.

Message deletion in mobile messaging apps allows people to “unsay” things they have said. This paper explores how and why people use (or do not use) this feature within remediation strategies after a communication error is identified. We present findings from a multi-stage survey designed to explore people’s general experiences of the message deletion feature (N = 401), peoples’ experiences of using this feature during the remediation of an error (N = 70), and receivers’ perceptions around recent message deletions (N = 68). While people are typically aware of the deletion feature, it is infrequently used. When used, it is primarily done so to improve conversations by reducing confusion between conversation partners. We found people being aware of message deletions creating information-gaps which can provoke curiosity in recipients, causing them to develop narratives to help address the uncertainty. We found concerns amongst senders that these narratives would be of a negative nature, having an undesirable impact on how others perceive them. We use our findings to suggest ways in which mobile messaging apps could improve conversational experiences around erroneous and regrettable messages.

As people's offline and online lives become increasingly entwined, the sensitivity of personal information disclosed online is increasing. Disclosures often occur through structured disclosure fields (e.g., drop-down lists). Prior research suggests these fields may limit privacy, with non-disclosing users being presumed to be hiding undesirable information. We investigated this around HIV status disclosure in online dating apps used by men who have sex with men. Our online study asked participants (N=183) to rate profiles where HIV status was either disclosed or undisclosed. We tested three designs for displaying undisclosed fields. Visibility of undisclosed fields had a significant effect on the way profiles were rated, and other profile information (e.g., ethnicity) could affect inferences that develop around undisclosed information. Our research highlights complexities around designing for non-disclosure and questions the voluntary nature of these fields. Further work is outlined to ensure disclosure control is appropriately implemented around online sensitive information disclosures.

HIV status disclosure fields in online sex-social applications ("apps") are designed to help increase awareness, reduce stigma, and promote sexual health. Public disclosure could also help those diagnosed relate to others with similar statuses to feel less isolated. However, in our interview study (n=28) with HIV positive and negative men who have sex with men (MSM), we found some users preferred to keep their status private, especially when disclosure could stigmatise and disadvantage them, or risk revealing their status to someone they knew offline in a different context. How do users manage these tensions between health, stigma, and privacy? We analysed our interview data using signalling theory as a conceptual framework and identify participants developing 'signal appropriation' strategies, helping them manage the disclosure of their HIV status. Additionally, we propose a set of design considerations that explore the use of signals in the design of sensitive disclosure fields.

Infertility can place a significant burden on couples and individuals when trying to conceive. Approximately 20-30% of all cases of infertility are due to male-related factors. Whatever the cause of difficulty in conceiving, little is known about how men find support when dealing with fertility issues, or when or how online resources are being used. This paper reports on a qualitative study of anonymous online posts (N=603) from forums related to fertility that are used by men. We analysed this data using thematic analysis to understand how men are using online forums as a resource when experiencing fertility issues. We found that online forums play a valued role in facilitating connections between men experiencing an often stigmatised condition. These forums offer men accessible and private spaces which allow for more open discussion, helping them to make sense of their situation. We discuss our findings in relation to Genuis and Bronstein's model of finding a "new normal" and present our elaborated model of finding a "new normal" in the context of experiencing fertility problems.

mHealth applications ("apps") must be searched for and downloaded prior to use, creating a potential barrier to uptake. Integrating health interventions into existing online social environments removes this barrier. However, little is known about the effects of linking sensitive health information to existing online identities. Our qualitative analysis of online comments (n=192) explores the user views of an HIV intervention integrated into the geosocial hookup app Grindr. We find some HIV positive users report keeping their status private to reduce their stigma exposure, whilst others report publicly disclosing their status to avoid being stigmatised by others. Where users keep their status private, we find concerns that social assumptions may develop around these non-disclosures, creating a privacy unraveling effect which restricts disclosure choice. Using Peppet's four proposed limits to privacy unraveling, we develop a set of descriptive conceptual designs to explore the privacy respecting potential of these limits within this context and propose further research to address this privacy challenge.