We examine how people experience two choices in the design of generative ghosts, AI systems that are trained on data of the dead: representation, where an AI speaks about a deceased person in the third person, and reincarnation, where the AI speaks as the deceased in the first person. Through a qualitative user study with 16 participants, we explore how each shaped authenticity, affect, and risk. Reincarnation was preferred for its immediacy, but participants shared fears of over-reliance. Representation was preferred for engaging with memory over conversational presence, though participants often ignored this distinction, engaging in dialogue despite third-person framing. Across both modes, participants privileged affective resonance over factual fidelity. We conclude by showing how factors such as tone, language, and conversational rhythm – factors unique to the user’s memory of the deceased – shape interactions with generative ghosts, and argue that those interactions are always collaborative.

After a break-up, people are left with data representative of their lost relationship - pictures, posts, and connections that exist because of that relationship. As part of breaking up and moving on, people often make decisions about managing that data. Prior work has identified two broad types of curatorial philosophies people adopt in data management: archivists and revisionists. However, what drives individuals to one approach remains unknown and is difficult to design sociotechnical systems for. Through focus group interviews with couples still together, we present a decision-making framework for data management. We outline factors that can influence individuals' decision to act as an archivist or revisionist in the wake of a break-up. From our data and framework, we identify six implications for design to improve user experiences in the wake of a break-up, and from those implications, offer concrete suggestions for design for social media platforms.

After a romantic relationship ends, individuals are left to deal with the digital remnants of the relationship. These possessions and connections pose difficulties for users -- they are identity markers of an identity that one may no longer want legible to their online audiences. Further, they can cause upsetting moments that might impede moving on from the break-up. Through interviews with 11 women who had had a recent break-up, this empirical study examined how people managed their online identity after their break-up. We found that people took different actions towards their possessions and connections in service of creating a post-break-up identity. Using Brubaker & Hayes's (2011) representational framework, we find users attempting to deal with connections as if they were possessions, creating tensions that our current systems are ill-suited to address. Turning to Hogan's (2010) framework of the online identity 'exhibition', we see users creating exhibitions for an audience of one -- themselves -- while also making decisions about who is allowed to see their public-facing exhibitions. We conclude by arguing that existing tools are ill-designed to support competing desires to present authentic past and future online identities and offer design suggestions for consideration.

Coming out and being visible online can offer transgender and/or non-binary people benefits not found elsewhere. However, it also can expose them to negative reactions and bad experiences. Through an analysis of 15 semi-structured interviews, we investigate the experiences of transgender and/or non-binary people coming out across social media sites (SMSs). We found that participants employed strategies around disclosure and visibility to limit the consequences of coming out and to access support. Using trans theory on visibility, we discuss how online spaces present metaphorical "doors" to resources, support, and recognition---but can also be "traps" for those that do not meet the expectations of the space. We discuss how visibility empowered participants to create "trapdoors" to new spaces within SMSs where they could create positive outcomes for themselves and their communities. We close with considerations for designers as they create online spaces, and present a broader call to action for the HCI community around designing online spaces.

Every day on social media, people see streams of content curated by algorithms that leverage their relationships, preferences, and identities. However, algorithms can oversimplify the complexity of people's social contexts. Consequently, algorithms can present content to people in ways that are insensitive to their circumstances. Through 19 in-depth interviews, our empirical study examines instances of contextually insensitive content through the lens of people's upsetting encounters with content about their ex-romantic partners on Facebook. We characterize the encounters our participants had with content about their exes, including where on Facebook it occurred, the types of social connections involved in the content, and participants' perceptions of why the content appeared. Based on our findings, we describe the "social periphery"---the complex social networks and data that enable inferred connections around otherwise explicit relationships---and discuss the design challenges that the periphery presents designers.

We present a comprehensive and structured review of 132 peer-reviewed publications between the years of 2008 and 2015 to inform Human-Computer Interaction (HCI) researchers and interaction designers about the current and multi-disciplinary knowledge on the topic of adolescent online safety and risks. Overall, we found that the existing literature has deeply studied the phenomena around adolescent online safety through an in-depth examination of the prevalence, perceptions, behaviors, characteristics, and outcomes associated with various online risk experiences. However, very few studies have moved beyond formative evaluations that inform design to novel design interventions or summative evaluations of new designs that serve to effectively change the status quo.