Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use.

Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items. To answer this question, we first conducted offline analyses (Study 1) on SuggestBot, a task-routing recommender system for Wikipedia, then did a three-month controlled experiment (Study 2). Our results show that presenting users with articles from underrepresented topics increased the proportion of work done on those articles without significantly reducing overall recommendation uptake. We discuss the implications of our results, including how ignoring the article discovery process can artificially narrow recommendations on peer production platforms.

For peer production communities to be sustainable, they must attract and retain new contributors. Studies have identified social and technical barriers to entry and discovered some potential solutions, but these solutions have typically focused on a single highly successful community, the English Wikipedia, been tested in isolation, and rarely evaluated through controlled experiments. We propose the Newcomer Homepage, a central place where newcomers can learn how peer production works and find opportunities to contribute, as a solution for attracting and retaining newcomers. The homepage was built upon existing research and designed in collaboration with partner communities. Through a large-scale controlled experiment spanning 27 non-English Wikipedia wikis, we evaluate the homepage and find modest gains, and that having a positive effect on the newcomer experience depends on the newcomer's context. We discuss how this impacts interventions that aim to improve the newcomer experience in peer production communities.

Wikipedia---like most peer production communities---suffers from a basic problem: the amount of work that needs to be done (articles to be created and improved) exceeds the available resources (editor effort). Recommender systems have been deployed to address this problem, but they have tended to recommend work tasks that match individuals' personal interests, ignoring more global community values. In English Wikipedia, discussion about Vital articles constitutes a proxy for community values about the types of articles that are most important, and should therefore be prioritized for improvement. We first analyzed these discussions, finding that an article's priority is considered a function of 1) its inherent importance and 2) its effects on Wikipedia's global composition. One important example of the second consideration is balance, including along the dimensions of gender and geography. We then conducted a quantitative analysis evaluating how four different article prioritization methods---two from prior research---would affect Wikipedia's overall balance on these two dimensions; we found significant differences among the methods. We discuss the implications of our results, including particularly how they can guide the design of recommender systems that take into account community values, not just individuals' interests.

Recent studies have identified various forms of bias in language-based models, raising concerns about the risk of propagating social biases against certain groups based on sociodemographic factors (e.g., gender, race, geography). In this study, we analyze the treatment of age-related terms across 15 sentiment analysis models and 10 widely-used GloVe word embeddings and attempt to alleviate bias through a method of processing model training data. Our results show significant age bias is encoded in the outputs of many sentiment analysis algorithms and word embeddings, and we can alleviate this bias by manipulating training data.

Computational approaches to text analysis are useful in understanding aspects of online interaction, such as opinions and subjectivity in text. Yet, recent studies have identified various forms of bias in language-based models, raising concerns about the risk of propagating social biases against certain groups based on sociodemographic factors (e.g., gender, race, geography). In this study, we contribute a systematic examination of the application of language models to study discourse on aging. We analyze the treatment of age-related terms across 15 sentiment analysis models and 10 widely-used GloVe word embeddings and attempt to alleviate bias through a method of processing model training data. Our results demonstrate that significant age bias is encoded in the outputs of many sentiment analysis algorithms and word embeddings. We discuss the models' characteristics in relation to output bias and how these models might be best incorporated into research.

Many applications of geotagged content are predicated on the concept of localness (e.g., local restaurant recommendation, mining social media for local perspectives on an issue). However, definitions of who is a "local" in a given area are typically informal and ad-hoc and, as a result, approaches for localness assessment that have been used in the past have not been formally validated. In this paper, we begin the process of addressing these gaps in the literature. Specifically, we (1) formalize definitions of "local" using themes identified in a 30-paper literature review, (2) develop the first ground truth localness dataset consisting of 132 Twitter users and 58,945 place-tagged tweets, and (3) use this dataset to evaluate existing localness assessment approaches. Our results provide important methodological guidance to the large body of research and practice that depends on the concept of localness and suggest means by which localness assessment can be improved.

The extensive Wikipedia literature has largely considered Wikipedia in isolation, outside of the context of its broader Internet ecosystem. Very recent research has demonstrated the significance of this limitation, identifying critical relationships between Google and Wikipedia that are highly relevant to many areas of Wikipedia-based research and practice. This paper extends this recent research beyond search engines to examine Wikipedia's relationships with large-scale online communities, Stack Overflow and Reddit in particular. We find evidence of consequential, albeit unidirectional relationships. Wikipedia provides substantial value to both communities, with Wikipedia content increasing visitation, engagement, and revenue, but we find little evidence that these websites contribute to Wikipedia in return. Overall, these findings highlight important connections between Wikipedia and its broader ecosystem that should be considered by researchers studying Wikipedia. Critically, our results also emphasize the key role that volunteer-created Wikipedia content plays in improving other websites, even contributing to revenue generation.

Much research has shown that social media platforms have substantial population biases. However, very little is known about how these population biases affect the many algorithms that rely on social media data. Focusing on the case study of geolocation inference algorithms and their performance across the urban-rural spectrum, we establish that these algorithms exhibit significantly worse performance for underrepresented populations (i.e. rural users). We further establish that this finding is robust across both text- and network-based algorithm designs. However, we also show that some of this bias can be attributed to the design of algorithms themselves rather than population biases in the underlying data sources. For instance, in some cases, algorithms perform badly for rural users even when we substantially overcorrect for population biases by training exclusively on rural data. We discuss the implications of our findings for the design and study of social media-based algorithms.

Local search entails looking for places, such as restaurants or hotels, in a geographically-constrained region. Within local search, it has been observed that an individual's familiarity with their environment (i.e. how well they know the area in a query of the form "{places} in {area}") impacts which places they are most interested in visiting. Less well-understood though is how people's information preferences differ during 1) different phases of the search process and 2) based on their level of familiarity. Through a series of surveys in the domain of dining, we explore how familiarity moderates what level of information is useful to an individual about restaurant location when choosing a place to visit. We further examine how these preferences vary between regions and phases of local search (deciding on a restaurant or determining how to go). We contribute an understanding of people's information preferences during search, building on prior research of how offline context impacts online needs.

Although Couchsurfing and Airbnb are both online communities that help users host strangers in their homes, they differ in an important sense: Couchsurfing prohibits monetary payment while Airbnb is built around it.We conducted interviews with users experienced on both Couchsurfing and Airbnb ("dual-users") to better understand systemic differences between the platforms. Based on these interviews we propose that, compared to Couchsurfing, Airbnb: (1) appears to require higher quality services, (2) places more emphasis on places over people, and (3) shifts social power from hosts to guests. Using public profiles from both platforms, we present analyses exploring each theme. Finally, we present evidence showing that Airbnb's growth has coincided with a decline in Couchsurfing. Taken together, our findings paint a complex picture of the changing character of network hospitality.

Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions.

Geotagged tweets and other forms of social media volunteered geographic information (VGI) are becoming increasingly critical to many applications and scientific studies. An important assumption underlying much of this research is that social media VGI is "local", or that its geotags correspond closely with the general home locations of its contributors. We demonstrate through a study on three separate social media communities (Twitter, Flickr, Swarm) that this localness assumption holds in only about 75% of cases. In addition, we show that the geographic contours of localness follow important sociodemographic trends, with social media in, for instance, rural areas and older areas, being substantially less local in character (when controlling for other demographics). We demonstrate through a case study that failure to account for non-local social media VGI can lead to misrepresentative results in social media VGI-based studies. Finally, we compare the methods for determining localness, finding substantial disagreement in certain cases, and highlight new best practices for social media VGI-based studies and systems.