As more users turn to video-sharing platforms like YouTube as an information source, they may consume misinformation despite their best efforts. In this work, we investigate ways that users can better assess the credibility of videos by first exploring how users currently determine credibility using existing signals on platforms and then by introducing and evaluating new credibility-based signals. We conducted 12 contextual inquiry interviews with YouTube users, determining that participants used a combination of existing signals, such as the channel name, the production quality, and prior knowledge, to evaluate credibility, yet sometimes stumbled in their efforts to do so. We then developed Viblio, a prototype system that enables YouTube users to view and add citations and related information while watching a video based on our participants’ needs. From an evaluation with 12 people, all participants found Viblio to be intuitive and useful in the process of evaluating a video’s credibility and could see themselves using Viblio in the future.

Software engineers’ unawareness of user feedback in earlier stages of design contributes to privacy issues in many products. Although extensive research exists on gathering and analyzing user feedback, there is limited understanding about how developers can integrate user feedback to improve product designs to better meet users’ privacy expectations. We use Zoom’s deprecated attendee attention tracking feature to explore issues with integrating user privacy feedback into software development, presenting public online critiques about this deprecated feature to 18 software engineers in semi-structured interviews and observing how they redesign this feature. Our results suggest that while integrating user feedback for privacy is potentially beneficial, it’s also fraught with challenges of polarized design suggestions, confirmation bias, and limited scope of perceived responsibility.

Predicting users’ privacy concerns is challenging due to privacy’s subjective and complex nature. Previous research demonstrated that generic attitudes, such as those captured by Westin’s Privacy Segmentation Index, are inadequate predictors of context-specific attitudes. We introduce ContextLabel, a method enabling practitioners to capture users’ privacy profiles across domains and predict their privacy concerns towards unseen data practices. ContextLabel’s key innovations are (1) using non-mutually exclusive labels to capture more nuances of data practices, and (2) capturing users’ privacy profiles by asking them to express privacy concerns to a few data practices. To explore the feasibility of ContextLabel, we asked 38 participants to express their thoughts in free text towards 13 distinct data practices across five days. Our mixed-methods analysis shows that a preliminary version of ContextLabel can predict users’ privacy concerns towards unseen data practices with an accuracy (73%) surpassing Privacy Segmentation Index (56%) and methods using categorical factors (59%).

User experience (UX) has undergone a revolution in collaborative practices, due to tools that enable quick feedback and continuous collaboration with a varied team across a design’s lifecycle. However, it is unclear how this shift in collaboration has been received in professional UX practice, and whether new pain points have arisen. To this end, we conducted a survey (N = 114) with UX practitioners at software organizations based in the U.S. to better understand their collaborative practices and tools used throughout the design process. We found that while an increase in collaborative activity enhanced many aspects of UX work, some long-standing challenges—such as handing off designs to developers—still persist. Moreover, we observed new challenges emerging from activities enabled by collaborative tools such as design system management. Based on our findings, we discuss how UX practices can improve collaboration moving forward and provide concrete design implications for collaborative UX tools.