Large language models (LLMs) have been widely evaluated on macro-scale geographic tasks, such as global factual recall, event summarization, and regional reasoning. Yet, their ability to handle hyper-local knowledge remains poorly understood. This gap is increasingly consequential as real-world applications, from civic platforms to community journalism, demand AI systems that can reason about neighborhood-specific dynamics, cultural narratives, and local governance. Existing benchmarks fall short in capturing this complexity, often relying on coarse-grained data or isolated references. We present LocalBench, the first benchmark designed to systematically evaluate LLMs on county-level local knowledge across the United States. Grounded in the Localness Conceptual Framework, LocalBench includes 14,782 validated question-answer pairs across 526 U.S. counties in 49 states, integrating diverse sources such as Census statistics, local subreddit discourse, and regional news. It spans physical, cognitive, and relational dimensions of locality. Using LocalBench, we evaluate 13 state-of-the-art LLMs under both closed-book and web-augmented settings. Our findings reveal critical limitations: even the best-performing models reach only 56.8% accuracy on narrative-style questions and perform below 15.5% on numerical reasoning. Moreover, larger model size and web augmentation do not guarantee better performance, for example, search improves Gemini's accuracy by +13.6%, but reduces GPT-series performance by -11.4%. These results underscore the urgent need for language models that can support equitable, place-aware AI systems: capable of engaging with the diverse, fine-grained realities of local communities across geographic and cultural contexts.

Broadband infrastructure is often assumed to reduce informational disparities by expanding access to digital platforms. Yet less is understood about how broadband shapes participation in peer production communities, where knowledge is collectively created and maintained. Using spatial regression models, we examine how broadband coverage influences who contributes and how participation patterns shift in geo-tagged Wikipedia edits across U.S. counties. We find that broadband expansion is strongly associated with increased contributions from local casual and regular editors while reducing reliance on bot-driven activity. However, contributions remain highly concentrated, as prolific editors continue to dominate production. Moreover, we uncover spatial spillover effects, where broadband gains in one county decrease participation in neighboring areas, revealing competitive dynamics in peer production. These findings challenge the assumption that access alone fosters equity, showing that broadband reshapes but does not evenly redistribute editorial influence, with implications for infrastructure policy, platform design, and sustaining inclusive peer production.

People are increasingly using Large Language Models (LLMs) for a sense of “localness,” yet their ability to accurately and equitably represent local knowledge remains unexamined. To investigate this, we conducted a large-scale evaluation using a benchmark of over 12,000 question-answer pairs spanning structured census data, local news, and social media. Our results show that performance is strongly shaped by data modality: structured tasks expose deep limitations in numerical reasoning and calibration, while open-ended prompts reveal a clear performance hierarchy favoring informal user-generated content over professionally edited prose. Our primary finding is the existence of deep, context-dependent disparities that affect communities differently. We uncover a dual geographic bias: in formal news contexts, models exhibit a strong “urban advantage,” leaving rural areas systematically underrepresented with lower semantic depth. Conversely, in social media data, models suffer an “urban penalty,” struggling to navigate the conversational complexity and slang of high-density areas. This indicates that while rural locales face a “poverty of data,” highly documented urban centers face a “poverty of precision.” We also identify a domain bias: models are more adept at handling concrete, physical questions but consistently struggle to capture the nuanced relational and cognitive dimensions of a community. This work provides the first systematic audit of localness disparities in LLMs, revealing how they reflect and risk amplifying real-world inequities. Achieving equitable local representation requires moving beyond passive evaluation to active intervention. We call for a concerted effort from the CSCW community to build richer and more ethical datasets, design interfaces that prioritize user verification over blind trust, and architect AI systems for deeper and more just engagement with place.

The proliferation of OpenStreetMap (OSM) as a collaborative geographic dataset has been instrumental in addressing data gaps globally. However, disparities in map coverage persist, particularly in economically disadvantaged and disaster-prone regions. The emergence of the Humanitarian OpenStreetMap Team (HOT) in 2010 aimed to bridge these gaps by leveraging the collective efforts of volunteers through platforms like the HOT Tasking Manager. While previous research has highlighted the success of these initiatives in recruiting contributors and expanding map coverage, their implications for existing structural biases remain unclear, potentially hindering the regions benefiting from humanitarian activities. Thus, our study employs the difference-in-difference-in-difference (DDD) approach to empirically examine the pattern between contribution dynamics and population density in project regions involved in humanitarian mapping activities. By further investigating the participation of various levels of contributors in projects with different population densities, we aim to inform better design strategies to align contributor expectations and experiences, fostering more equitable and effective humanitarian mapping efforts.

Peer production systems like OpenStreetMap exhibit persistent information gaps across socioeconomic and population density lines, posing challenges for end users, especially as AI tools such as autonomous vehicles rely on this incomplete data. While prior work has established these trends, their impact on the production of critical semantic metadata remains unclear. In this study, we focus on three OpenStreetMap metadata tags that are essential for supporting road safety. Contrary to the expected socioeconomic and population density trends, our findings reveal that cultural factors play a significant role in influencing tag production. Moreover, we find that automated contributions may hinder human tag production in OpenStreetMap, despite the potential of these automated imports to address data gaps. Overall, our results add nuance to the understanding of the trade-offs involved in automated imports and shed light on how both practitioners and the public can more effectively improve metadata coverage.

Millions of people move for work yearly, but this labor migration risks social and cultural challenges, hindering migrants' integration into new communities. Software tools could support this transition, but the design space around, and the mechanisms behind, how individuals develop spatial understanding and 'sense of place' is unclear. In our study, we leverage mental maps to explore migrants' 'sense of place'. We conduct a mixed- methods study with 12 participants, spanning two sessions - one before and one after their relocation, totaling 24 data sessions. We discover that post-relocation, mental maps not only widen coverage and generalization but also decrease in cartographic complexity and accuracy, reflecting a nuanced blend of personal narratives and spatial awareness. We also find that strategies for rebuilding and reshaping 'sense of place' span a complex set of dimensions spanning personal, social and environmental challenges, post-move. Our findings lay the groundwork, and underscore the need, for 'platial' (versus spatial) understanding and tools to rebuild sense of place, and foster better community cohesion. We highlight design opportunities for creating tools, especially those capturing personal nuances, to help migrants reestablish themselves and their sense of place.

Social media platforms use permanent suspension as a measure of last resort to intervene with users who spread harmful or misleading content. However, permanent suspension does not signify the end of a user's online presence, but rather on that specific platform. This issue is particularly salient for influential users with large audiences, as they have the potential to cause substantial shifts in the overall social media information landscape when suspended. Our work employs a mixed methods approach to study the context around, and behavioral patterns after, permanent suspension. We find that migration is a common step after suspension, and characterize a number of behavioral strategies and patterns that occur after influential users are suspended. By focusing on consequences of suspension across more holistically, we have identified numerous opportunities for design and future research to mitigate the potential negative effects of permanent suspensions on the broader social media information landscape.

CSCW and HCI scholars are increasingly adopting asset-based approaches to community-based social computing research. Emerging from asset-based community development (ABCD), an approach to community and economic development research and practice that emerged in the 1990s, advocates for asset-based approaches argue that a "needs-based approach" to development is overly focused on community deficits, and in doing so portrays communities in a largely negative light. Using ABCD methods, social computing scholars work to aid communities in identifying, classifying, and deploying their unrealized assets through sociotechnical systems. But researchers deploying asset-based approaches in CSCW and HCI more broadly have yet to grapple with the origins of ABCD in neoliberal economic shifts of the 1980s and 1990s that perpetuated distrust in the state and sought to transfer development power to private, local actors. Drawing on a case study of controversy around the construction of a cell tower in a very remote and rural community in the Midwestern United States to examine the inherent tradoffs present in asset- and deficit-based research.

Microtasking, the decomposition of tasks into small units of work, is prolific in human computation and crowdsourcing. Some peer production systems are beginning to leverage this same technique in volunteer contribution-based settings. While early research suggests that focusing volunteer work in this way using microtasking may be fruitful, the effects of microtasking on contributor behavior in volunteer peer production settings, like OpenStreetMap, remain unclear. This paper takes advantage of a natural experiment facilitated by the Humanitarian OpenStreetMap's Tasking Manager, a microtasking system, and employs causal inference analysis to evaluate the effects of a microtasking intervention on contributor dynamics. Our study systematically leverages a global dataset to analyze peer production dynamics, building on prior research to address a gap in peer production literature and informing the design of the microtasking interfaces. We causally show that, indeed, microtasking can be an effective intervention in peer production settings, but it may exacerbate power-law patterns that are common in such settings. By further analyzing project design decisions and characteristics, we develop implications for platform practitioners, with a focus on addressing engagement and contribution inequity issues prevalent in settings like OpenStreetMap.

Wikipedia is the product of thousands of editors working collaboratively to provide free and up-to-date encyclopedic information to the project's users. This article asks to what degree Wikipedia articles in three languages - Hindi, Urdu, and English - achieve Wikipedia's mission of making neutrally-presented, reliable information on a polarizing, controversial topic available to people around the globe. We chose the topic of the recent revocation of Article 370 of the Constitution of India, which, along with other recent events in and concerning the region of Jammu and Kashmir, has drawn attention to related articles on Wikipedia. This work focuses on the English Wikipedia, being the preeminent language edition of the project, as well as the Hindi and Urdu editions. Hindi and Urdu are the two standardized varieties of Hindustani, a lingua franca of Jammu and Kashmir. We analyzed page view and revision data for three Wikipedia articles to gauge popularity of the pages in our corpus, and responsiveness of editors to breaking news events and problematic edits. Additionally, we interviewed editors from all three language editions to learn about differences in editing processes and motivations, and we compared the text of the articles across languages as they appeared shortly after the revocation of Article 370. Across languages, we saw discrepancies in article tone, organization, and the information presented, as well as differences in how editors collaborate and communicate with one another. Nevertheless, in Hindi and Urdu, as well as English, editors predominantly try to adhere to the principle of neutral point of view (NPOV), and for the most part, the editors quash attempts by other editors to push political agendas.

Wikipedia is the product of thousands of editors working collaboratively to provide free and up-to-date encyclopedic information to its users. This article asks to what degree Wikipedia articles in three languages --- Hindi, Urdu, and English --- achieve Wikipedia's mission of making neutrally-presented, reliable information on a polarizing, controversial topic available to people around the globe. We chose the topic of the recent revocation of Article 370 of the Constitution of India, which, along with other recent events in and concerning the region of Jammu and Kashmir, has drawn attention to related articles on Wikipedia. This work focuses on the English Wikipedia, being the preeminent language edition of the project, as well as the Hindi and Urdu editions. Hindi and Urdu are the two standardized varieties of Hindustani, a lingua franca of Jammu and Kashmir. We analyzed page view and revision data for three Wikipedia articles. Additionally, we interviewed editors from all three Wikipedias to learn differences in editing processes and motivations. While activity on South Asian language editions of Wikipedia is growing, at the time of writing, the Hindi and Urdu editions are still in their nascency. In Hindi and Urdu, as well as English, editors predominantly adhere to the principle of neutral point of view (NPOV), and these editors quash attempts by other editors to push political agendas.

Expert investigators bring advanced skills and deep experience to analyze visual evidence, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise. In this paper, we introduce the concept of shared representations for crowd--augmented expert work, focusing on the complex sensemaking task of image geolocation performed by professional journalists and human rights investigators. We built GroundTruth, an online system that uses three shared representations-a diagram, grid, and heatmap-to allow experts to work with crowds in real time to geolocate images. Our mixed-methods evaluation with 11 experts and 567 crowd workers found that GroundTruth helped experts geolocate images, and revealed challenges and success strategies for expert-crowd interaction. We also discuss designing shared representations for visual search, sensemaking, and beyond.

Volunteered Geographic Information (VGI), such as contributions to OpenStreetMap and geotagged Wikipedia articles, is often assumed to be produced locally. However, recent work has found that peer-produced VGI is frequently contributed by non-locals. We evaluate this approach across hundreds of content types from Wikipedia, OpenStreetMap, and eBird, and show that these models can describe more than 90% of "VGI flows" for some content types. Our findings advance geographic HCI theory, suggesting some spatial mechanisms underpinning VGI production. We also discuss design implications that can help (a) human and algorithmic consumers of VGI evaluate the perspectives it contains and (b) address geographic coverage variations in these platforms (e.g. via more effective volunteer recruitment strategies).

The evolution of contributor behavior in peer production communities over time has been a subject of substantial interest in the social computing community. In this paper, we extend this literature to the geographic domain, exploring contribution behavior in OpenStreetMap using a spatiotemporal lens. In doing so, we observe a geographic version of a 'born, not made' phenomenon: throughout their lifespans, contributors are relatively consistent in the places and types of places that they edit. We show how these 'born, not made' trends may help explain the urban and socioeconomic coverage biases that have been observed in OpenStreetMap. We also discuss how our findings can help point towards solutions to these biases.

Peer production communities create valuable content such as software, encyclopedia articles, and map data. As part of the creation process, these communities define production standards for their content, e.g., semantic and syntactic requirements. We carried out a study in OpenStreetMap to investigate the role of that community's standards for geographic metadata. We found that most applied metadata was consistent with the community's standards; however, we also found that the standards identified many opportunities for applying metadata that were not achieved. In addition, when we situated the standards in the context of OpenStreetMap's data model, we found a significant amount of ambiguity; the syntax allowed only one value, but everyday meaning -- and the standards themselves -- called for multiple values. Our results suggest significant opportunities for OpenStreetMap to produce additional valuable open source content to power applications.

The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of location-based gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (e.g. the game advantages urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically-linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.

In addition to encyclopedia articles and software, peer production communities produce structured data, e.g., Wikidata and OpenStreetMap's metadata. Structured data from peer production communities has become increasingly important due to its use by computational applications, such as CartoCSS, MapBox, and Wikipedia infoboxes. However, this structured data is usable by applications only if it follows standards. We did an interview study focused on OpenStreetMap's knowledge production processes to investigate how -- and how successfully -- this community creates and applies its data standards. Our study revealed a fundamental tension between the need to produce structured data in a standardized way and OpenStreetMap's tradition of contributor freedom. We extracted six themes that manifested this tension and three overarching concepts, correctness, community, and code, which help make sense of and synthesize the themes. We also offered suggestions for improving OpenStreetMap's knowledge production processes, including new data models, sociotechnical tools, and community practices (e.g. stronger leadership).

CoCoRaHS is a multinational citizen science project for observing precipitation. Like many citizen science projects, volunteer retention is a key measure of engagement and data quality. Through survival analysis, we found that participant age (self-reported at account creation) is a significant predictor of retention. Compared to all other age groups, participants aged 60-70 are much more likely to sign up for CoCoRaHS, and to remain active for several years. We also measured the influence of task difficulty and the relative frequency of rain, finding small but statistically significant and counterintuitive effects. Finally, we confirmed previous work showing that participation levels within the first month are highly predictive of eventual retention. We conclude with implications for observational citizen science projects and crowdsourcing research in general.

As the gig economy continues to grow and freelance work moves online, five-star reputation systems are becoming more and more common. At the same time, there are increasing accounts of race and gender bias in evaluations of gig workers, with negative impacts for those workers. We report on a series of four Mechanical Turk-based studies in which participants who rated simulated gig work did not show race- or gender bias, while manipulation checks showed they reliably distinguished between low- and high-quality work. Given prior research, this was a striking result. To explore further, we used a Bayesian approach to verify absence of ratings bias (as opposed to merely not detecting bias). This Bayesian test let us identify an upper- bound: if any bias did exist in our studies, it was below an average of 0.2 stars on a five-star scale. We discuss possible interpretations of our results and outline future work to better understand the results.

Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography—distance decay, structured variation in population density, mental maps, and “the Big Sort” (spatial homophily)—manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.

Mobile crowdsourcing markets (e.g., Gigwalk and TaskRabbit) offer crowdworkers tasks situated in the physical world (e.g., checking street signs, running household errands). The geographic nature of these tasks distinguishes these markets from online crowdsourcing markets and raises new, fundamental questions. We carried out a controlled study in the Chicago metropolitan area aimed at addressing two key questions: (1) What geographic factors influence whether a crowdworker will be willing to do a task? (2) What geographic factors influence how much compensation a crowdworker will demand in order to do a task? Quantitative modeling shows that travel distance to the location of the task and the socioeconomic status (SES) of the task area are important factors. Qualitative analysis enriches our modeling, with workers mentioning safety and difficulties getting to a location as key considerations. Our results suggest that low-SES areas are currently less able to take advantage of the benefits of mobile crowdsourcing markets. We discuss the implications of our study for these markets, as well as for "sharing economy" phenomena like UberX, which have many properties in common with mobile crowdsourcing markets.

Session identification is a common strategy used to develop metrics for web analytics and perform behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or has advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that the regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity.