Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as password-protected chats and end-to-end encryption (E2EE), which primarily protect message contents. Beyond contents, a lot can be inferred about people simply by tracing who sends and receives messages, when, and how often. This paper explores user perceptions of and attitudes toward 'untraceability', defined as preventing third parties from tracing who communicates with whom, to inform the design of privacy-enhancing technologies and untraceable communication protocols. Through a vignette-based qualitative study with 189 participants, we identify a diverse set of features that users perceive to be useful for untraceable messaging, ranging from using aliases instead of real names to VPNs. Through a reflexive thematic analysis, we uncover three overarching attitudes that influence the support or rejection of untraceability in messaging platforms and that can serve as a set of new privacy personas: privacy fundamentalists, who advocate for privacy as a universal right; safety fundamentalists, who support surveillance for the sake of accountability; and optimists, who advocate for privacy in principle but also endorse exceptions in idealistic ways, such as encryption backdoors. We highlight a critical gap between the threat models assumed by users and those addressed by untraceable communication protocols. In particular, many participants understood untraceability as a form of anonymity, but interpret it as senders and receivers hiding their identities from each other, rather than from external network observers. We discuss implications for the design of strategic communication and user interfaces of untraceable messaging protocols, and propose framing untraceability as a form of 'altruistic privacy', i.e., adopting privacy-enhancing technologies to protect others, as a promising strategy to foster broader adoption.

Surveys are a widespread method for collecting data at scale, but their rigid structure often limits the depth of qualitative insights obtained. While interviews naturally yield richer responses, they are challenging to conduct across diverse locations and large participant pools. To partially bridge this gap, we investigate the potential of using LLM-based chatbots to support qualitative data collection through interview probes embedded in surveys. We assess four theory-based interview probes: descriptive, idiographic, clarifying, and explanatory. Through a split-plot study design (N=64), we compare the probes' impact on response quality and user experience across three key stages of HCI research: exploration, requirements gathering, and evaluation. Our results show that probes facilitate the collection of high-quality survey data, with specific probes proving effective at different research stages. We contribute practical and methodological implications for using chatbots as research tools to enrich qualitative data collection.

Successful collaboration in computer-mediated teams requires awareness among group members of each other’s knowledge, skills, and goals. Large Language Models (LLMs) can play a mediating role in establishing and maintaining this awareness among group members. In an in-situ study, we explored the impact of an LLM-based chatbot on cognitive and social group awareness through a distributed text-based group task. We instructed participants (N = 48) to complete a travel-planning task in sixteen groups of three, with each member given conflicting goals. Each chat was complemented by a chatbot that could be asked for assistance. Through a survey and semi-structured interview, we gained insight into participants’ deliberations on the task and the chatbot’s role. We found that the chatbot’s presence helped increase group awareness as users are forced to clearly and transparently formulate their intentions when prompting the chatbot. The chatbot’s ability to provide suggestions that compromise between user goals based on the chat history helped participants reach a consensus. We present implications for the design of chatbots for collaborative settings.

Emoji are a popular, expressive form of non-verbal communication. However, people often use emoji in ways that result in confusing or cumbersome screen reader output. We created two accessibility support designs: (1) Preview, which displays a basic text transcript of a message with emoji that a screen reader would narrate, and (2) Alert, which summarises potential accessibility issues caused by emoji within a message. We explored our designs using an online survey and provided participants with the option to edit messages that contained emoji, should they choose to do so. We collected 1508 modified messages from 116 sighted participants and conducted a qualitative analysis of the data to identify the strategies participants used when asked to edit a message for accessibility issues and their appreciation of each design. We found that participants preferred the Preview design over Alert since it allows for subjective interpretations of what constitutes an accessible message. We report sighted users' rewriting strategies (e.g., editing the message to move the emoji to the end) and incorrect assumptions about screen readers that would lead to using textual markers that are incompatible with screen readers. We discuss the design implications for future systems for accessible messaging.

Computational media describes a vision of software, which, in contrast to application-centric software, is (1) malleable, so users can modify existing functionality, (2) computable, so users can run custom code, (3) distributable, so users can open documents across different devices, and (4) shareable, so users can easily share and collaborate on documents. Over the last ten years, the Webstrates and Codestrates projects aimed at realizing this vision of computational media. Webstrates is a server application that synchronizes the DOM of websites. Codestrates builds on top of Webstrates and adds an authoring environment, which blurs the use and development of applications. Grounded in a chronology of the development of Webstrates and Codestrates, we present eight tensions that we needed to balance during their development. We use these tensions as an analytical lens in three case studies and a game challenge in which participants created games using Codestrates. We discuss the results of the game challenge based on these tensions and present key takeaways for six of them. Finally, we present six lessons learned from our endeavor to realize the vision of computational media, demonstrating the balancing act of weighing the vision against the pragmatics of implementing a working system.

In January 2021, WhatsApp announced an update to their privacy policy, sparking an outcry that saw millions of users install other messaging apps such as Telegram and Signal. This presented a rare opportunity to study users’ experiences when trying to leave the world’s most popular communication app. We conducted surveys in February and May with 1525 WhatsApp users from Mexico, Spain, South Africa, and the United Kingdom. Over a quarter wanted to switch at least part of their communication to other apps, but 74% of them failed to do so. By May, 27% had increased their use of other apps, and only 16% used WhatsApp less. Beyond network effects, users struggled with making informed choices of alternative apps and with differences in their design and functionality. We suggest messaging interoperability as an approach to alleviate switching costs and discuss implications for HCI research and competition regulation of digital services.

Co-customizations are collaborative customizations in messaging apps that all conversation members can view and change, e.g. the color of chat bubbles on Facebook Messenger. Co-customizations grant new opportunities for expressing intimacy; however, most apps offer private customizations only. To investigate how people in close relationships integrate co-customizations into their established communication app ecosystems, we built DearBoard: an Android keyboard that allows two people to co-customize its color theme and a toolbar of expression shortcuts (emojis and GIFs). In a 5-week field study with 18 pairs of couples, friends, and relatives, participants expressed their shared interests, history, and knowledge of each other through co-customizations that served as meaningful decorations, interface optimizations, conversation themes, and non-verbal channels for playful, affectionate interactions. The co-ownership of the co-customizations invited participants to negotiate who customizes what and for whom they customize. We discuss how co-customizations mediate intimacy through place-making efforts and suggest design opportunities.

In hybrid meetings, multiple co-located participants communicate with remote participants through video. But video communication inhibits non-verbal cues, and this often causes remote participants to feel excluded. To address this issue, we built MirrorBlender: a What-You-See-Is-What-I-See video-conferencing system for blending, repositioning, and resizing mirrors. Mirrors here denote shared video feeds of people and screens. In a qualitative study of MirrorBlender with three hybrid meeting sessions, we found that the shared control of mirrors supported users in negotiating a blended interpersonal space. Moreover, it enabled diverse acts of inclusion of remote participants. In particular, remote participants brought attention to themselves by manipulating the position, scale, and translucency of their camera and screen feeds. Participants also embodied and leveraged their mirror images for deictic gestures and playful interactions. Based on these findings, we discuss new opportunities for supporting video-mediated collaboration.

Couples exhibit special communication practices, but apps rarely offer couple-specific functionality. Research shows that sharing streams of contextual information (e.g. location, motion) helps couples coordinate and feel more connected. Most studies explored a single, ephemeral stream; we study how couples' communication changes when sharing multiple, persistent streams. We designed Lifelines, a mobile-app technology probe that visualizes up to six streams on a shared timeline: closeness to home, battery level, steps, media playing, texts and calls. A month-long study with nine couples showed that partners interpreted information mostly from individual streams, but also combined them for more nuanced interpretations. Persistent streams allowed missing data to become meaningful and provided new ways of understanding each other. Unexpected patterns from any stream can trigger calls and texts, whereas seeing expected data can replace direct communication, which may improve or disrupt established communication practices. We conclude with design implications for mediating awareness within couples.

The growing adoption of emojis, stickers and GIFs suggests a corresponding demand for rich, personalized expression in messaging apps. Some people customize apps to enable more personal forms of expression, yet we know little about how such customizations shape everyday communication. Since people increasingly communicate via multiple apps side-by-side, we are also interested in how customizing one app influences communication via other apps. We created a taxonomy of customization options based on interviews with 15 "extreme users" of communication apps. We found that participants tailored their apps to express their identities, organizational culture, and intimate bonds with others. They also experienced expression breakdowns: frustrations around barriers to transferring personal forms of expression across apps, which inspired inventive workarounds to maintain cross-app habits of expression, such as briefly switching apps to generate and export content for a particular conversation. We conclude with implications for personalized expression in ecosystems of communication apps.

Although users accomplish ever more tasks on touch-enabled mobile devices, gesture-based interaction remains limited and almost never customizable by users. Our goal is to help users create gestures that are both personally memorable and reliably recognized by a touch-enabled mobile device. We address these competing requirements with two dynamic guides that use progressive feedforward to interactively visualize the "negative space" of unused gestures: the Pathward technique suggests four possible completions to the current gesture, and the Fieldward technique uses color gradients to reveal optimal directions for creating recognizable gestures. We ran a two-part experiment in which 27 participants each created 42 personal gesture shortcuts on a smartphone, using Pathward, Fieldward or No Feedforward. The Fieldward technique best supported the most common user strategy, i.e. to create a memorable gesture first and then adapt it to be recognized by the system. Users preferred the Fieldward technique to Pathward or No Feedforward, and remembered gestures more easily when using the technique. Dynamic guides can help developers design novel gesture vocabularies and support users as they design custom gestures for mobile applications.

Today's users communicate via multiple apps, even when they offer almost identical functionality. We studied how and why users distribute their contacts within their app ecosystem. We found that the contacts in an app affect a user's conversations with other contacts, their communication patterns in the app, and the quality of their social relationships. Users appropriate the features and technical constraints of their apps to create idiosyncratic communication places, each with its own recursively defined membership rules, perceived purposes, and emotional connotations. Users also shift the boundaries of their communication places to accommodate changes in their contacts' behaviour, the dynamics of their relationships, and the restrictions of the technology. We argue that communication apps should support creating multiple communication places within the same app, relocating conversations across apps, and accessing functionality from other apps.

CommandBoard offers a simple, efficient and incrementally learnable technique for issuing gesture commands from a soft keyboard. We transform the area above the keyboard into a command-gesture input space that lets users draw unique command gestures or type command names followed by execute. Novices who pause see an in-context dynamic guide, whereas experts simply draw. Our studies show that CommandBoard's inline gesture shortcuts are significantly faster (almost double) than markdown symbols and significantly preferred by users. We demonstrate additional techniques for more complex commands, and discuss trade-offs with respect to the user's knowledge and motor skills, as well as the size and structure of the command space.