The objective of this study was to investigate and classify the roles, or distinct contribution styles, adopted by participants within the OpenStreetMap (OSM) community. Using a quantitative analysis of mapping behaviors, we devised a methodology to identify distinct features associated with specific roles. We used an unsupervised clustering approach and unveiled eight discernible roles, or types of mapper in OSM. Each role displays specific patterns of mapping behaviors related to their habits and preferences for adding or editing map objects over time. We validated our roles, in part, using known affiliations with humanitarian and corporate organizations. Using these roles, we examine community composition and contributor retention over time. Our contributions include applying existing methods on the analysis of contributor behavior in online platforms to OSM, the identification of eight roles that can guide future research and design within OSM, and further understanding into the overall trajectory of the world’s largest geospatial peer production community.

Vaccine hesitancy has always been a public health concern, and anti-vaccine campaigns that proliferate disinformation have gained traction across the US in the last 25 years. The demographics of resistance are varied, with health, religious, and, increasingly, political concerns cited as reasons. With the COVID-19 pandemic igniting the fastest development of vaccines to date, mis- and disinformation about them have become inflammatory, with campaigning allegedly including racial targeting. Through a primarily qualitative investigation, this study inductively examines a large online vaccine discussion space that invokes references to the unethical Tuskegee Syphilis Study to understand how tactics of racial targeting of Black Americans might appear publicly. We find that such targeting is entangled with a genuine discussion about medical racism and vaccine hesitancy. Across 12 distinct voices that address race, medical racism, and vaccines, we discuss how mis- and disinformation sit alongside accurate information in a “polyvocal” space.

OpenStreetMap (OSM) is a free and openly-editable database of geographic information. Over the years, OSM has evolved into the world's largest open knowledge base of geospatial data, and protecting OSM from the risk of vandalized and falsified information has become paramount to ensuring its continued success. However, despite the increasing usage of OSM and a wide interest in vandalism detection on open knowledge bases such as Wikipedia and Wikidata, OSM has not attracted as much attention from the research community, partially due to a lack of publicly available vandalism corpus. In this paper, we report on the construction of the first OSM vandalism corpus, and release it publicly. We describe a user embedding approach to create OSM user embeddings and add embedding features to a machine learning model to improve vandalism detection in OSM. We validate the model against our vandalism corpus, and observe solid improvements in key metrics. The validated model is deployed into production for vandalism detection on Daylight Map.

Today, disaster events are mobilizing digital volunteers to meet the data needs of those on the ground. One form of this crowd work is Volunteered Geographic Information. This peer-produced spatial data creates the most up-to-date map of the affected region; maintaining the accuracy of these data is therefore a critical task. Accuracy is one aspect of data quality, a relative concept requiring standards to measure against. The field of Geographic Information Sciences has developed standards for this comparison, achieving widespread acceptance. However, the peer production model of spatial data presents new opportunities—and challenges—to traditional methods of quality assessment. Through analysis of the OpenStreetMap database, we show that temporal editing patterns and contributor characteristics can provide additional means of understanding spatial data quality. Drawing upon experiences from Wikipedia, we offer and evaluate three intrinsic quality metrics of peer-produced spatial data to assess the quality of contributions to OpenStreetMap for crisis response.

OpenStreetMap (OSM) is the most widely used volunteer geographic information system. Although it is increasingly relied upon during humanitarian response as the most up-to-date, accurate, or accessible map of affected areas, the behavior of the mappers who contribute to it is not well understood. In this paper, we explore the work practices and interactions of volunteer mappers operating in the high-tempo, high-volume context of disasters. To do this, we built upon and expanded prior network analysis techniques to select high-value portions of the vast OSM data for further qualitative analysis. We then performed detailed content analysis of the identified activity and, where possible, conducted interviews with the participants. This research allowed the identification of seven distinct mapping practices that can be classified according to dimensions of time, space, and interpersonal interaction. Our work represents a baseline for future research about how OSM crisis mapping practices have evolved over time.

OpenStreetMap (OSM) is a volunteer-driven, globally distributed organization whose members work to create a common digital map of the world. OSM embraces ideals of open data, and to that end innovates both socially and technically to develop practices and processes for coordinated operation. This paper provides a brief history of OSM and then, through quantitative and qualitative examination of the OSM database and other sites of articulation work, examines organizational growth through the lens of two catastrophes that spurred enormous humanitarian relief responses-the 2010 Haiti Earthquake and the 2013 Typhoon Yolanda. The temporally- and geographically- constrained events scope analysis for what is a rapidly maturing, whole-planet operation. The first disaster identified how OSM could support other organizations responding to the event. However, to achieve this, OSM has had to refine mechanisms of collaboration around map creation, which were tested again in Typhoon Yolanda. The transformation of work between these two events yields insights into the organizational development of large, data-producing online organizations.