YouTube has many features, such as homepage recommendations, that encourage users to explore its vast library of videos. However, when users visit YouTube with a specific intention, e.g., learning how to program in Python, these features to encourage exploration are often distracting. Prior work has innovated ‘commitment interfaces’ that restrict social media but finds that they often indiscriminately block needed content. In this paper, we describe the design, development, and evaluation of an ‘adaptable commitment interface,’ the SwitchTube mobile app, in which users can toggle between two interfaces when watching YouTube videos: Focus Mode (search-first) and Explore Mode (recommendations-first). In a three-week field deployment with 46 US participants, we evaluate how the ability to switch between interfaces affects user experience, finding that it provides users with a greater sense of agency, satisfaction, and goal alignment. We conclude with design implications for how adaptable commitment interfaces can support digital wellbeing.

Well-intentioned users sometimes enable the spread of misinformation due to limited context about where the information originated and/or why it is spreading. Building upon recommendations based on prior research about tackling misinformation, we explore the potential to support media literacy through platform design. We develop and design an intervention consisting of a tweet trajectory-to illustrate how information reached a user-and contextual cues-to make credibility judgments about accounts that amplify, manufacture, produce, or situate in the vicinity of problematic content (AMPS). Using a research through design approach, we demonstrate how the proposed intervention can help discern credible actors, challenge blind faith amongst online friends, evaluate the cost of associating with online actors, and expose hidden agendas. Such facilitation of credibility assessment can encourage more responsible sharing of content. Through our findings, we argue for using trajectory-based designs to support informed information sharing, advocate for feature updates that nudge users with reflective cues, and promote platform-driven media literacy.

In the attention economy, video apps employ design mechanisms like autoplay that exploit psychological vulnerabilities to maximize watch time. Consequently, many people feel a lack of agency over their app use, which is linked to negative life effects such as loss of sleep. Prior design research has innovated external mechanisms that police multiple apps, such as lockout timers. In this work, we shift the focus to how the internal mechanisms of an app can support user agency, taking the popular YouTube mobile app as a test case. From a survey of 120 U.S. users, we find that autoplay and recommendations primarily undermine sense of agency, while playlists and search support it. From 13 co-design sessions, we find that when users have a specific intention for how they want to use YouTube they prefer interfaces that support greater agency. We discuss implications for how designers can help users reclaim a sense of agency over their media use.

Collaborative qualitative coding often involves coders assign- ing different labels to the same instance, leading to ambiguity. We refer to such an instance of ambiguity as disagreement in coding. Analyzing reasons for such a disagreement is essential-- both for purposes of bolstering user understanding gained from coding and reinterpreting the data collaboratively, and for negotiating user-assigned labels for building effective machine learning models. We propose a conceptual definition of collective disagreement using diversity and divergence within the coding distributions. This perspective of disagreement translates to diverse coding contexts and groups of coders irrespective of discipline. We introduce two tree-based ranking metrics as standardized ways of comparing disagreements in how data instances have been coded. We empirically validate that, of the two tree-based metrics, coders' perceptions of dis- agreement match more closely with the n-ary tree metric than with the post-traversal tree metric.

People are increasingly sharing information on social media during disaster events. This information could be valuable to emergency responders, but there remain challenges for using it to inform response efforts---including filtering relevant information from the large volumes of noise. Previous research has largely focused on identifying information that can contribute to a generalized concept of situational awareness. Our work explores the value of approaching this problem from a different perspective---one of actionablity---with the idea that information relevance may vary across responder role, domain, and other factors. This approach asks how we can get the right information to the right person at the right time? We interviewed and surveyed diverse responders to understand what "actionable" information is, allowing that actionability might differ from one responder to another. Through the findings, we (a) offer a nuanced understanding of actionability and differentiate it from situational awareness; (b) describe responders' perspective of what distinguishes good information when making rapid judgments; and (c) suggest opportunities for augmenting social media use to highlight information that needs immediate attention. We offer researchers an opportunity to frame different models of actionability to suit the requirements of a responding role.

This paper contributes to the growing literature on peer-to-peer (P2P) applications through an ethnographic study of auto-rickshaw drivers in Bengaluru, India. We describe how the adoption of a P2P application, Ola, which connects passengers to rickshaws, changes drivers work practices. Ola is part of the 'peer services' phenomenon which enable new types of ad-hoc trade in labour, skills and goods. Auto-rickshaw drivers present an interesting case because prior to Ola few had used Smartphones or the Internet. Furthermore, as financially vulnerable workers in the informal sector, concerns about driver welfare become prominent. Whilst technologies may promise to improve livelihoods, they do not necessarily deliver [57]. We describe how Ola does little to change the uncertainty which characterizes an auto drivers' day. This leads us to consider how a more equitable and inclusive system might be designed.

When a user learns to use a new device, her understanding of it evolves. A progressive comparison of the evolving user models towards the device target model, for analysing learning, involves determining the behavioral proximity between them. To quantify the gap between a user model and a target model, we introduce an edit distance metric for measuring their behavioral proximity using a bisimulation-based equivalence relation. We define edit distance to be the minimum number of edges and states with incident edges required to be deleted from and/or added to a user model to make it bisimilar to the target model. We propose an algorithm to compute edit distance between two models and employ the heuristic procedure on experimental data for computing edit distance between target and user models. The data is organised into two experiments depending on the device the user interacted with: (a) a simple device resembling a vending machine and (b) a close to real-world vehicle transmission model. The results validate our proposed metric as edit distance converges with progressive user learning, increases for erroneous learning, and remains unchanged indicating no learning.