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Open data programs have become increasingly established at national and local levels of government. While the degree of success these programs have had in achieving their objectives remains open to question, one factor that has been identified as important to any success is the role of open data intermediaries, individuals and organizations that help others to make use of open data. In this paper we investigate how people become engaged with open data, what their motivations are, and the barriers and facilitators program participants perceive with regard to using open data effectively. We interview participants from a variety of backgrounds with differing levels of experience and engagement with open data. Participants include students learning how to train others in open data techniques and tools; people who attend open data events and use open data for commercial or social benefit; and representatives from local government, municipal agencies and a civic tech non-profit. We identify pathways to successfully developing and nurturing a community of open data intermediaries, and make five recommendations for organizations planning and managing open data programs. more...

This paper investigates the tools and practices used by Orientation and Mobility (O&M) specialists in instructing people who are blind or have low vision in concepts, skills, and techniques for safe and independent travel. Based on interviews with experienced instructors who practice in different O&M settings we find that a shortage of qualified specialists and restrictions on in-person activities during COVID-19 has accelerated interest in remote instruction and assessment, while widespread adoption of smartphones with accessibility support has driven interest in assistive apps. This presents both opportunities and challenges for a practice that is traditionally conducted in-person and assessed through qualitative observations. In response we identify multiple opportunities for HCI research in service of O&M, including: supporting a 'physician's assistant' model of remote O&M instruction and assessment, matching O&M instructors' clients with guide dogs, highlighting clients' progress towards O&M goals, and collaboratively planning routes and monitoring clients' independent travel progress. more...

Noise pollution is among the most consistently cited and highest impact quality-of-life issues in major urban areas across the US, with more than 70 million people estimated to be exposed to noise levels considered harmful. While HCI and CSCW has a relatively rich history of engagement with monitoring such environmental concerns, e.g. through participatory sensing, prior research has not to our knowledge engaged with the process of municipal mitigation. In this paper we present research in this direction, connecting support for pervasive environmental monitoring with civic engagement in mitigation action. Having first identified and described the research space for this engagement, we present empirical data from an ongoing case study focused on two communities living with chronic problem noise. We probe the experiences of residents and representatives of different municipal organizations tasked with addressing their concerns. We find that making and drawing on records of noise is important to residents and authorities, that these groups have misaligned perceptions of how effective current reporting programs are, and that communication between them can be poor. We also find that noise is often one part of more complex issues. We then discuss our findings in light of prior research, and identify a model of civic sensing that highlights opportunities for HCI design and research including: supporting residents' coordinated actions and actions with municipal open data, mediating residents' and authorities' assessments of data quality, and supporting accountability and attributable mitigation action. more...

In this work we explore confidence elicitation methods for crowdsourcing "soft" labels, e.g., probability estimates, to reduce the annotation costs for domains with ambiguous data. Machine learning research has shown that such "soft" labels are more informative and can reduce the data requirements when training supervised machine learning models. By reducing the number of required labels, we can reduce the costs of slow annotation processes such as audio annotation. In our experiments we evaluated three confidence elicitation methods: 1) "No Confidence" elicitation, 2) "Simple Confidence" elicitation, and 3) "Betting" mechanism for confidence elicitation, at both individual (i.e., per participant) and aggregate (i.e., crowd) levels. In addition, we evaluated the interaction between confidence elicitation methods, annotation types (binary, probability, and z-score derived probability), and "soft" versus "hard" (i.e., binarized) aggregate labels. Our results show that both confidence elicitation mechanisms result in higher annotation quality than the "No Confidence" mechanism for binary annotations at both participant and recording levels. In addition, when aggregating labels at the recording level, results indicate that we can achieve comparable results to those with 10-participant aggregate annotations using fewer annotators if we aggregate "soft" labels instead of "hard" labels. These results suggest that for binary audio annotation using a confidence elicitation mechanism and aggregating continuous labels we can obtain higher annotation quality, more informative labels, with quality differences more pronounced with fewer participants. Finally, we propose a way of integrating these confidence elicitation methods into a two-stage, multi-label annotation pipeline. more...

Recent years are seeing a sharp increase in the availability of personal omic (e.g. genomes, microbiomes) data to non-experts through direct-to-consumer testing kits. While the scientific understanding of human -omic information is evolving, the interpretation of the data may impact well-being of users and relevant others, and therefore poses challenges and opportunities for CSCW research. We identify the information, interaction, and sense-making needs of microbiomic data users, within the broader context of social omics - the sharing and collaborative engagement with data and interpretation. Analyzing users' discussions on Reddit's r/HumanMicrobiome, we identified seven user needs for microbiome data: reviewing an annotated report, comparing microbiome data, tracking changes, receiving personalized actionable information, curating and securing information, documenting and sharing self experiments, and enhancing the communication between patients and health-care providers. We highlight the ways in which users interact with each other to collaboratively make sense of the data. We conclude with design implications, including tools for better communication with care providers, and for symptom-centered sharing and discussion. more...

Audio annotation is key to developing machine-listening systems; yet, effective ways to accurately and rapidly obtain crowdsourced audio annotations is understudied. In this work, we seek to quantify the reliability/redundancy trade-off in crowdsourced soundscape annotation, investigate how visualizations affect accuracy and efficiency, and characterize how performance varies as a function of audio characteristics. Using a controlled experiment, we varied sound visualizations and the complexity of soundscapes presented to human annotators. Results show that more complex audio scenes result in lower annotator agreement, and spectrogram visualizations are superior in producing higher quality annotations at lower cost of time and human labor. We also found recall is more affected than precision by soundscape complexity, and mistakes can be often attributed to certain sound event characteristics. These findings have implications not only for how we should design annotation tasks and interfaces for audio data, but also how we train and evaluate machine-listening systems. more...

Peer production communities often struggle to retain contributors beyond initial engagement. This may be a result of contributors' level of motivation, as it is deeply intertwined with activity. Existing studies on participation focus on activity dynamics but overlook the accompanied changes in motivation. To fill this gap, this study examines the interplay between contributors' fun motives and activity over time. We combine motivational data from two surveys of Wikipedia newcomers with data of two periods of editing activity. We find that persistence in editing is related to fun, while the amount of editing is not: individuals who persist in editing are characterized by higher fun motives early on (when compared to dropouts), though their motives are not related to the number of edits made. Moreover, we found that newcomers' experience of fun was reinforced by their amount of activity over time: editors who were initially motivated by fun entered a virtuous cycle, whereas those who initially had low fun motives entered a vicious cycle. Our findings shed new light on the importance of early experiences and reveal that the relationship between motivation and participation levels is more complex than previously understood. more...