Visualizations play a considerable role in explaining trends or providing evidence when consuming data online. Whether those visualizations are shared on news outlets or social networks, platforms usually allow readers to discuss their stories in comments sections. For the scope of this work, we studied the online communityr/dataisbeautiful on Reddit. We found that chart creators were using a variety of authoring tools to share their content. Readers of these posts,commenters, used text mainly to discuss and critique the visual content. We noticed a need for a richer mode of communication that would show instead of telling authors what to do. Based on our findings, we introducedVizdat, a lightweight tool and extension to allow users to visualize and reproduce charts in the comments sections of data stories. We usedVizdat as atechnology probe with 11 Reddit users to create data visualization and discuss charts onr/dataisbeautiful. During the four-week field deployment period, we observed howVizdat was used and interviewed the participants. We found thatcommenters saw value in accessing the visualization specifications throughVizdat and used those charts to structure their replies with richer modalities. As a result, visualizationauthors appreciated the feedback and less toxic discussion provided through comments embedded with modified versions of their charts. In our paper, we share these findings and other insights to understand the dynamics of forum discussion around charts.

A new approach to online discussion, which situates student discussions in the margins of the course content, can enhance student engagement with course materials. However, in high-enrollment classes, the large number of comments can overwhelm and intimidate students. Some become frustrated by the volume of potential online interactions and by a perceived lack of immediate relevance to their studies. Likewise, instructors are disappointed when outstanding discussions, that they deem valuable for all to see, get lost in the clutter. To address these challenges, we propose visual spotlighting mechanisms for increasing the saliency of selected comments. We piloted and deployed multiple designs in two high-enrollment biology courses at a large public university in the United States. Interviews, surveys, and a controlled experiment show that spotlighting relevant comments in heavily annotated texts positively affects students' engagement, measured in terms of their attention to comments, and their reported sense of validation and pride. Students also reported their preferences for certain spotlighting designs.

Machine learning (ML) has become a crucial component in software products, either as part of the user experience or used internally by software teams. Prior studies have explored how ML is affecting development team roles beyond data scientists, including user experience designers, program managers, developers and operations engineers. However, there has been little investigation of how team members in different roles on the team communicate about ML, in particular about the quality of models. We use the general term quality to look beyond technical issues of model evaluation, such as accuracy and overfitting, to any issue affecting whether a model is suitable for use, including ethical, engineering, operations, and legal considerations. What challenges do teams face in discussing the quality of ML models? What work practices mitigate those challenges? To address these questions, we conducted a mixed-methods study at a large software company, first interviewing15 employees in a variety of roles, then surveying 168 employees to broaden our understanding. We found several challenges, including a mismatch between user-focused and model-focused notions of performance, misunderstandings about the capabilities and limitations of evolving ML technology, and difficulties in understanding concerns beyond one's own role. We found several mitigation strategies, including the use of demos during discussions to keep the team customer-focused.

Users have long struggled to extract and repurpose data from websites by laboriously copying or scraping content from web pages. An alternative is to write scripts that pull data through APIs. This provides a cleaner way to access data than scraping; however, APIs are effortful for programmers and nigh-impossible for non-programmers to use. In this work, we empower users to access APIs without programming. We evolve a schema for declaratively specifying how to interact with a data API. We then develop ScrAPIr: a standard query GUI that enables users to fetch data through any API for which a specification exists, and a second GUI that lets users author and share the specification for a given API. From a lab evaluation, we find that even non-programmers can access APIs using ScrAPIr, while programmers can access APIs 3.8 times faster on average using ScrAPIr than using programming.