Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study (n = 28), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm (n = 10) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

Social media platforms have rapidly adopted algorithmic curation with little consideration for the potential harm to users' mental well-being. We present findings from design workshops with 21 participants diagnosed with mental illness about their interactions with social media platforms. We find that users develop cause-and-effect explanations, or folk theories, to understand their experiences with algorithmic curation. These folk theories highlight a breakdown in algorithmic design that we explain using the framework of entanglement, a phenomenon where there is a disconnect between users' actions and platform outcomes on an emotional level. Participants' designs to address entanglement and mitigate harms centered on contextualizing their engagement and restoring explicit user control on social media. The conceptualization of entanglement and the resulting design recommendations have implications for social computing and recommender systems research, particularly in evaluating and designing social media platforms that support users' mental well-being.

Learning is essential for the long-term sustainability of apprenticeship-based communities; it lets novices gradually progress to experts through active engagement. However, the use of large language models (LLMs) can disrupt this trajectory: if novices rely on LLMs to generate content, they risk short-circuiting the learning opportunities embedded in the process of creating content themselves. To address this tension, we created WikiCoach, a Q&A–style LLM system designed to scaffold learning-through-doing in Wikipedia. WikiCoach does not generate content directly; instead it supports editors via three stages: 1) introducing relevant community policies and guidelines, 2) operationalizing them into actionable steps, and 3) providing evaluative feedback on initial drafts. We conducted a within-subjects study (N=28) comparing WikiCoach with a baseline content generation model, and found that WikiCoach significantly improved policy understanding and adherence to community norms, while increasing critical thinking and cognitive engagement. Participants also preferred WikiCoach for future editing tasks. Qualitative analysis revealed how WikiCoach shaped users’ expectations and interactions. Our findings demonstrate that AI systems can simultaneously support task completion and learning, offering a future direction for responsibly sustaining apprenticeship-based communities in the era of generative AI.

Video conferencing meetings are more effective when they are inclusive, but inclusion often hinges on meeting leaders' and/or co-facilitators' practices. AI systems can be designed to improve meeting inclusion at scale by moderating negative meeting behaviors and supporting meeting leaders. We explored this design space by conducting $9$ user-centered ideation sessions, instantiating design insights in a prototype ``virtual co-host'' system, and testing the system in a formative exploratory lab study ($n=68$ across $12$ groups, $18$ interviews). We found that ideation session participants wanted AI agents to ask questions before intervening, which we formalized as the ``Observe, Ask, Intervene'' (OAI) framework. Participants who used our prototype preferred OAI over fully autonomous intervention, but rationalized away the virtual co-host's critical feedback. From these findings, we derive guidelines for designing AI agents to influence behavior and mediate group work. We also contribute methodological and design guidelines specific to mitigating inequitable meeting participation.

Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers—a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge.org received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.

Online community research routinely poses minimal risk to individuals, but does the same hold true for online communities? In response to high-profile breaches of online community trust and increased debate in the social computing research community on the ethics of online community research, this paper investigates community-level harms and benefits of research. Through 9 participatory-inspired workshops with four critical online communities (Wikipedia, InTheRooms, CaringBridge, and r/AskHistorians), we found researchers should engage more directly with communities' primary purpose by rationalizing their methods and contributions in the context of community goals to equalize the beneficiaries of community research. To facilitate deeper alignment of these expectations, we present the FACTORS (Functions for Action with Communities: Teaching, Overseeing, Reciprocating, and Sustaining) framework for ethical online community research. Finally, we reflect on our findings by providing implications for researchers and online communities to identify and implement functions for navigating community-level harms and benefits.

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.

Workplace bias creates negative psychological outcomes for employees, permeating the larger organization. Workplace meetings are frequent, making them a key context where bias may occur. Video conferencing (VC) is an increasingly common medium for workplace meetings; we therefore investigated how VC tools contribute to increasing or reducing bias in meetings. Through a semi-structured interview study with 22 professionals, we found that VC features push meeting leaders to exercise control over various meeting parameters, giving leaders an outsized role in affecting bias. We demonstrate this with respect to four core VC features---user tiles, raise hand, text-based chat, and meeting recording---and recommend employing at least one of two mechanisms for mitigating bias in VC meetings---1) transferring control from meeting leaders to technical systems or other attendees and 2) helping meeting leaders better exercise the control they do wield.

Let me begin by saying that “avoiding adverse autonomous agent actions” seems like a very important goal! We all know about HAL and Skynet, right? Or maybe we were reassured by Asimov’s Three Laws ...

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.

Structured data peer production (SDPP) platforms like Wikidata play an important role in knowledge production. Compared to traditional peer production platforms like Wikipedia, Wikidata data is more structured and intended to be used by machines, not (directly) by people; end-user interactions with Wikidata often happen through intermediary "invisible machines." Given this distinction, we wanted to understand Wikidata contributor motivations and how they are affected by usage invisibility caused by the machine intermediaries. Through an inductive thematic analysis of 15 interviews, we find that: (i) Wikidata editors take on two archetypes---Architects who define the ontological infrastructure of Wikidata, and Masons who build the database through data entry and editing; (ii) the structured nature of Wikidata reveals novel editor motivations, such as an innate drive for organizational work; (iii) most Wikidata editors have little understanding of how their contributions are used, which may demotivate some. We synthesize these insights to help guide the future design of SDPP platforms in supporting the engagement of different types of editors.

Much prior research has found gender bias in peer production systems like Wikipedia and OpenStreetMap. This bias affects both women’s participation in these platforms and content about women on these platforms. We investigated the gender content gap in Wikidata, where less than 22% of items that represent people are about women. We asked: what is the source of this bias? Specifically, does it originate from the actions of Wikidata editors or from external factors; that is, does it simply reflect existing real world gender bias? We conducted a quantitative case study that found: (i) the most popular categories of people included in Wikidata represent male-dominant professions, such as American football; (ii) within a selected set of professions where we could obtain gender distribution data, Wikidata is no more biased than the real world: men and women are included at similar percentages, and the quality of items representing men and women also is similar. We provide possible explanations for our findings and implications for addressing the Wikidata content gap.

Spirituality is an understudied topic in social computing; however, for Online Health Community (OHC) users facing life-threatening illness, it is of fundamental importance. Through in-depth focus groups with OHC stakeholders in a US context, we derive a definition of "spiritual support" for use by designers and researchers who study online social support. We show that spiritual support is an integral dimension that underlies other social support types, and that if we ignore spirituality in design, we fail to mitigate problematic issues that arise in online spaces when users' spiritual values clash. Based on participants' ideations, we provide design implications for OHCs and other social media to better facilitate spiritual support through: (1) representing spiritual beliefs, (2) assistance with supportive communication, (3) support network visualization and mobilization, and (4) advance care planning and digital legacy.

Online technologies offer great promise to expand models of delivery for therapeutic interventions to help users cope with increasingly common mental illnesses like anxiety and depression. For example, "cognitive reappraisal" is a skill that involves changing one's perspective on negative thoughts in order to improve one's emotional state. In this work, we present Flip*Doubt, a novel crowd-powered web application that provides users with cognitive reappraisals ("reframes") of negative thoughts. A one-month field deployment of Flip*Doubt with 13 graduate students yielded a data set of negative thoughts paired with positive reframes, as well as rich interview data about how participants interacted with the system. Through this deployment, our work contributes: (1) an in-depth qualitative understanding of how participants used a crowd-powered cognitive reappraisal system in the wild; and (2) detailed codebooks that capture informative context about negative input thoughts and reframes. Our results surface data-derived hypotheses that may help to explain what types of reframes are helpful for users, while also providing guidance to future researchers and developers interested in building collaborative systems for mental health. In our discussion, we outline implications for systems research to leverage peer training and support, as well as opportunities to integrate AI/ML-based algorithms to support the cognitive reappraisal task. (Note: This paper includes potentially triggering mentions of mental health issues and suicide.)

Artificial intelligence algorithms have been used to enhance a wide variety of products and services, including assisting human decision making in high-stake contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for designers to understand algorithms and design products or services in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their goals and needs. We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating.

On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions. However, algorithms can fail to solve the problems they were designed for if they conflict with the values of communities who use them. In this study, we take a Value-Sensitive Algorithm Design approach to understanding a community-created and -maintained machine learning-based algorithm called the Objective Revision Evaluation System (ORES)---a quality prediction system used in numerous Wikipedia applications and contexts. Five major values converged across stakeholder groups that ORES (and its dependent applications) should: (1) reduce the effort of community maintenance, (2) maintain human judgement as the final authority, (3) support differing peoples' differing workflows, (4) encourage positive engagement with diverse editor groups, and (5) establish trustworthiness of people and algorithms within the community. We reveal tensions between these values and discuss implications for future research to improve algorithms like ORES.

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community---CaringBridge.org---where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships---patterns of repeated interactions---are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.

Group projects are an essential component of teaching user interface (UI) design. We identified six challenges in transferring traditional group projects into the context of Massive Open Online Courses: managing dropout, avoiding free-riding, appropriate scaffolding, cultural and time zone differences, and establishing common ground. We present a case study of the design of a group project for a UI Design MOOC, in which we implemented technical tools and social structures to cope with the above challenges. Based on survey analysis, interviews, and team chat data from the students over a six-month period, we found that our socio-technical design addressed many of the obstacles that MOOC learners encountered during remote collaboration. We translate our findings into design implications for better group learning experiences at scale.

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.

Bots have been important to peer production's success. Wikipedia, OpenStreetMap, and Wikidata all have taken advantage of automation to perform work at a rate and scale exceeding that of human contributors. Understanding the ways in which humans and bots behave in these communities is an important topic, and one that relies on accurate bot recognition. Yet, in many cases, bot activities are not explicitly flagged and could be mistaken for human contributions. We develop a machine classifier to detect previously unidentified bots using implicit behavioral and other informal editing characteristics. We show that this method yields a high level of fitness under both formal evaluation (PR-AUC: 0.845, ROC-AUC: 0.985) and a qualitative analysis of "anonymous" contributor edit sessions. We also show that, in some cases, unflagged bot activities can significantly misrepresent human behavior in analyses. Our model has the potential to support future research and community patrolling activities.

Emoji are popular in digital communication, but they are rendered differently on different viewing platforms (e.g., iOS, Android). It is unknown how many people are aware that emoji have multiple renderings, or whether they would change their emoji-bearing messages if they could see how these messages render on recipients' devices. We developed software to expose the multi-rendering nature of emoji and explored whether this increased visibility would affect how people communicate with emoji. Through a survey of 710 Twitter users who recently posted an emoji-bearing tweet, we found that at least 25% of respondents were unaware that the emoji they posted could appear differently to their followers. Additionally, after being shown how one of their tweets rendered across platforms, 20% of respondents reported that they would have edited or not sent the tweet. These statistics reflect millions of potentially regretful tweets shared per day because people cannot see emoji rendering differences across platforms. Our results motivate the development of tools that increase the visibility of emoji rendering differences across platforms, and we contribute our cross-platform emoji rendering software to facilitate this effort.

Most commonly used approaches to developing automated or artificially intelligent algorithmic systems are Big Data-driven and machine learning-based. However, these approaches can fail, for two notable reasons: (1) they may lack critical engagement with users and other stakeholders; (2) they rely largely on historical human judgments, which do not capture and incorporate human insights into how the world can be improved in the future. We propose and describe a novel method for the design of such algorithms, which we call Value Sensitive Algorithm Design. Value Sensitive Algorithm Design incorporates stakeholders' tacit knowledge and explicit feedback in the early stages of algorithm creation. This increases the chance to avoid biases in design choices or to compromise key stakeholder values. Generally, we believe that algorithms should be designed to balance multiple stakeholders' needs, motivations, and interests, and to help achieve important collective goals. We also describe a specific project "Designing Intelligent Socialization Algorithms for WikiProjects in Wikipedia" to illustrate our method. We intend this paper to contribute to the rich ongoing conversation concerning the use of algorithms in supporting critical decision-making in society.

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).

Productive and dedicated members are critical to the success of online production communities like Wikipedia. Many communities organize in subgroups where members voluntarily work on projects of shared interest. In this paper, we investigate how members' pre-joining connections with the subgroup predict their productivity and withdrawal after joining. Drawing insights from attachment theories in social psychology, we examine two types of pre-joining connections: textit{identity-based} attachment (how much members' interests were aligned with the subgroup's topics) and textit{bonds-based} attachment (how much members had interacted with other members of the subgroup). Analyses of 79,704 editors in 1,341 WikiProjects show that 1) both identity-based and bonds-based attachment increased editors' post-joining productivity and reduced their likelihood of withdrawal; 2) identity-based attachment had a stronger effect on boosting direct contributions to articles while bonds-based attachment had a stronger effect on increasing article and project coordination, and reducing member withdrawal.

The technical barriers for conversing with recommender systems using natural language are vanishing. Already, there are commercial systems that facilitate interactions with an AI agent. For instance, it is possible to say "what should I watch" to an Apple TV remote to get recommendations. In this research, we investigate how users initially interact with a new natural language recommender to deepen our understanding of the range of inputs that these technologies can expect. We deploy a natural language interface to a recommender system, we observe users' first interactions and follow-up queries, and we measure the differences between speaking- and typing-based interfaces. We employ qualitative methods to derive a categorization of users' first queries (objective, subjective, and navigation) and follow-up queries (refine, reformulate, start over). We employ quantitative methods to determine the differences between speech and text, finding that speech inputs are typically longer and more conversational.

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.

Nearly any group is subject to turnover : some people leave, while others join. Turnover can be especially high in online groups, since participation typically is strictly voluntary. We investigated the effects of member turnover in online groups, specifically in Wikipedia's WikiProjects. We based our studies on theories from organizational science, which suggest that it is not just the amount of turnover, but the characteristics of those leaving and those joining that matter. We characterized leavers and newcomers by their prior productivity, tenure (in the group or community), and participation in other groups within the larger community. Furthermore, we considered the moderating effect of group size on turnover. We analyzed data from 88,427 editors who participated in 1,054 WikiProjects, finding that (1) the positive effects of newcomers to a group were larger than the negative effects of leavers, (2) prior productivity, tenure, and participation in other groups all played significant roles, and (3) the effects of leavers and newcomers were amplified in larger groups.

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.

We introduce a theoretical framework called precision crowdsourcing whose goal is to help turn online information consumers into information contributors. The framework looks at the timing and nature of the requests made of users and the feedback provided to users with the goal of increasing long-term contribution and engagement in the site or system. We present the results of a field experiment in which almost 3000 users were asked to tag movies (plus a null control group) as we varied the selection of task (popular/obscure), timing of requests (immediate or varying delays), and relational rhetoric (neutral, system reciprocal, other users reciprocal) of the requests. We found that asking increases tags provided overall, though asking generally decreases the provision of unprompted tags. Users were more likely to comply with our request when we asked them to tag obscure movies and when we used reciprocal request rhetoric.

Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag-based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.

To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by expressing preferences for individual items - this process is an inefficient way to convert a user's effort into improved personalization. Rather, we propose that new users can begin by expressing their preferences for groups of items. We test this idea by designing and evaluating an interactive process where users express preferences across groups of items that are automatically generated by clustering algorithms. We contribute a strategy for recommending items based on these preferences that is generalizable to any collaborative filtering-based system. We evaluate our process with both offline simulation methods and an online user experiment. We find that, as compared with a baseline rate-15-items interface, (a) users are able to complete the preference elicitation process in less than half the time, and (b) users are more satisfied with the resulting recommended items. Our evaluation reveals several advantages and other trade-offs involved in moving from item-based preference elicitation to group-based preference elicitation.

Pinterest is a popular social networking site that lets people discover, collect, and share pictures of items from the Web. Among popular social media sites, Pinterest has by far the most skewed gender distribution: women are four times more likely than men to use it. To better understand this, we examined two factors that generally affect whether people try a social site and whether they continue using it: the external perception of a site (e.g., as conveyed in popular media) and the site's initial user experience. For the latter, we focused on the role of social bootstrapping, importing contacts from one social site to another. We conducted a survey study, finding that: perceptions of Pinterest among users and non-users of the site differed significantly; trying Pinterest led to substantial changes in user perceptions of the site; social bootstrapping affected users' initial impression of Pinterest, generally improving it for women and harming it for men. We present implications of our findings for design and research.

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.

Peer production communities have been proven to be successful at creating valuable artefacts, with Wikipedia as a prime example. However, a number of studies have shown that work in these communities tends to be of uneven quality and certain content areas receive more attention than others. In this paper, we examine the efficacy of a range of targeted strategies to increase the quality of under-attended content areas in peer production communities. Mining data from five quality improvement projects in the English Wikipedia, the largest peer production community in the world, we show that certain types of strategies (e.g. creating artefacts from scratch) have better quality outcomes than others (e.g. improving existing artefacts), even if both are done by a similar cohort of participants. We discuss the implications of our findings for Wikipedia as well as other peer production communities.

The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like "show more popular items". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.

Studies have shown that the recommendation of unseen, novel or serendipitous items is crucial for a satisfying and engaging user experience. As a result, recent developments in recommendation research have increasingly focused towards introducing novelty in user recommendation lists. While, existing solutions aim to find the right balance between the similarity and novelty of the recommended items, they largely ignore the user needs for novelty. In this paper, we show that there are large individual and temporal differences in the users' novelty preferences. We develop a regression model to predict these dynamic novelty preferences of users using features derived from their past interactions. Finally, we describe an adaptive recommender,~\emph{adaNov-R}, that adapts to the user needs for novel items and show that the model achieves better recommendation performance on a metric that considers both novel and familiar items.

Wikipedia, the encyclopedia "anyone can edit", has become increasingly less so. Recent academic research and popular discourse illustrates the often aggressive ways newcomers are treated by veteran Wikipedians. These are complex sociotechnical issues, bound up in infrastructures based on problematic ideologies. In response, we worked with a coalition of Wikipedians to design, develop, and deploy Snuggle, a new user interface that served two critical functions: making the work of newcomer socialization more effective, and bringing visibility to instances in which Wikipedians? current practice of gatekeeping socialization breaks down. Snuggle supports positive socialization by helping mentors quickly find newcomers whose good-faith mistakes were reverted as damage. Snuggle also supports ideological critique and reflection by bringing visibility to the consequences of viewing newcomers through a lens of suspiciousness.

Pinterest is a popular social curation site where people collect, organize, and share pictures of items. We studied a fundamental issue for such sites: what patterns of activity attract attention (audience and content reposting)-- We organized our studies around two key factors: the extent to which users specialize in particular topics, and homophily among users. We also considered the existence of differences between female and male users. We found: (a) women and men differed in the types of content they collected and the degree to which they specialized; male Pinterest users were not particularly interested in stereotypically male topics; (b) sharing diverse types of content increases your following, but only up to a certain point; (c) homophily drives repinning: people repin content from other users who share their interests; homophily also affects following, but to a lesser extent. Our findings suggest strategies both for users (e.g., strategies to attract an audience) and maintainers (e.g., content recommendation methods) of social curation sites.

Most people associate with people like themselves, a process called homophily. Exposure to diversity, however, makes us more informed as individuals and as a society. In this paper, we investigate political disagreements on Facebook to explore the conditions under which diverse opinions can coexist online. Via a mixed methods approach comprising 103 survey responses and 13 interviews with politically engaged American social media users, we found that participants who perceived more differences with their friends engaged less on Facebook than those who perceived more homogeneity. Weak ties were particularly brittle to political disagreements, despite being the ties most likely to offer diversity. Finally, based on our findings we suggest potential design opportunities to bridge across ideological difference: 1) support exposure to weak ties; and 2) make common ground visible while friends converse.

Under contribution is an important problem in online social production communities: important tasks don't get done, and only a small minority of participants are active contributors. How can we remedy this situation? We explore the feasibility of using the act of consuming information as a gateway to contributing information; specifically, we investigate semi-automated means to extract useful information from standard types of user feedback. We explore this approach in the context of a geographic wiki and route-planning system for bicyclists. We analyzed naturally occurring textual route feedback, finding that the feedback was rich in information such as bikeability ratings, tags and notes that are useful to improve the system's route finding and navigational assistance capabilities. We also present a technique to extract such information by engaging users in dialogue immediately after they obtain a route. We believe that our results and ideas are applicable to a broad class of social production systems.

The "real world" nature of field-based citizen science involves unique data management challenges that distinguish it from projects that involve only Internet-mediated activities. In particular, many data contribution and review practices are often accomplished "offline' via paper or general-purpose software like Excel. This can lead to integration challenges when attempting to implement project-specific ICT with full revision and provenance tracking. In this work, we explore some of the current challenges and opportunities in implementing ICT for managing volunteer monitoring data. Our two main contributions are: a general outline of the workflow tasks common to field-based data collection, and a novel data model for preserving provenance metadata that allows for ongoing data exchange between disparate technical systems and participant skill levels. We conclude with applications for other domains, such as hydrologic forecasting and crisis informatics, as well as directions for future research.

In open content communities like Wikipedia and StackOverflow and in open source software projects, a small proportion of users produce a majority of the content and take on much of the required community maintenance work. Understanding this class of users is crucial to creating and sustaining healthy communities. We carried out a mixed-method study of core contributors to the Cyclopath geographic wiki and bicycle routing web site. We present our findings and organize our discussion using concepts from activity theory. We found that the Cyclopath core contributors aren't the dedicated cyclists and that the characteristics of the community shape the site, the rules, and the tools for contributing. Additionally, we found that numerous aspects about the surrounding ecology of related systems and communities may help to shape how the site functions and views itself. We draw implications for future research and design from these findings.

Eli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. Online recommender systems - built on algorithms that attempt to predict which items users will most enjoy consuming - are one family of technologies that potentially suffers from this effect. Because recommender systems have become so prevalent, it is important to investigate their impact on users in these terms. This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users. To the best of our knowledge, it is the first paper to measure the filter bubble effect in terms of content diversity at the individual level. We contribute a novel metric to measure content diversity based on information encoded in user-generated tags, and we present a new set of methods to examine the temporal effect of recommender systems on the user experience. We do find that recommender systems expose users to a slightly narrowing set of items over time. However, we also see evidence that users who actually consume the items recommended to them experience lessened narrowing effects and rate items more positively.

In this paper, we build on our previous work by evaluating several approaches for assessing the skill of players and teams on the basis of both individual performance and group cohesion, or "team chemistry", using game data from the National Basketball Association (NBA). Previously developed for skill assessment in team-based multi-player video games (e.g., Halo 3), we find that group cohesion is a predictive feature in virtual and real-world team-based games, and that methods utilizing such features can often outperform the baseline in both contexts. Additionally, we observe a strong positive correlation between the predictive accuracy of our group cohesion-based approaches and the duration of playing time between a particular configuration of players on a team and their opponents, or "match-up" length.

Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well as loci of intense research interest. Recently, a new site has exploded into prominence: Pinterest became the fastest social network to reach 10M users, growing 4000% in 2011 alone. While many Pinterest articles have appeared in the popular press, there has been little scholarly work so far. In this paper, we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What role does gender play in the site's social connections? And finally, what distinguishes Pinterest from existing networks, in particular Twitter? In short, we find that being female means more repins, but fewer followers, and that four verbs set Pinterest apart from Twitter: use, look, want and need. This work serves as an early snapshot of Pinterest that later work can leverage.

Social psychology offers several theories of potential use for designing techniques to increase user contributions to online communities. Some of these techniques follow the "compliance without pressure" approach, where users are led to comply with a request without being subjected to any obvious external pressure. We evaluated two such techniques -- foot-in-the-door and low-ball -- in the context of Cyclopath, a geographic wiki. We found that while both techniques succeeded, low-ball elicited more work than foot-in-the-door. We discuss design and research implications of applying these (and other such techniques) in online communities.

Administrators of online communities face the crucial issue of understanding and developing their user communities. Will new users become committed members? What types of roles are particular individuals most likely to take on? We report on a study that investigates these questions. We administered a survey (based on standard psychological instruments) to nearly 4000 new users of the MovieLens film recommendation community from October 2009 to March 2010 and logged their usage history on MovieLens. We found that general volunteer motivations, pro-social behavioral history, and community-specific motivations predicted both the amount of use and specific types of activities users engaged in after joining the community. These findings have implications for the design and management of online communities.

Corporate meetings are increasingly being held remotely using web technologies. With such remote meetings being recorded and made available after the fact, there is a pressing need for tools to access and utilize these recordings efficiently. Our work explores the utility of using annotations generated by meeting attendees to meet this need. We conducted a controlled lab study to evaluate the benefits of sharing annotations. Attendee-created annotations were shared with non-attendees to assist them on typical information retrieval tasks. Results indicate that (a) non-attendees given access to shared annotations performed about as well as attendees provided with their own and shared annotations, (b) non-attendees were more confident in their responses when they used shared annotations as access cues into the recording than when they directly skimmed the video, and (c) attendees utilized shared annotations more than their own, with similar success and confidence as using their own annotations.

Users have come to rely on automated route finding services for driving, public transit, walking, and bicycling. Current state of the art route finding algorithms typically rely on objective factors like time and distance; they do not consider subjective preferences that also influence route quality. This paper addresses that need. We introduce a new framework for evaluating edge rating prediction techniques in transportation networks and use it to explore ten families of prediction algorithms in Cyclopath, a geographic wiki that provides route finding services for bicyclists. Overall, we find that personalized algorithms predict more accurately than non-personalized ones, and we identify two algorithms with low error and excellent coverage, one of which is simple enough to be implemented in thin clients like web browsers. These results suggest that routing systems can generate better routes by collecting and analyzing users' subjective preferences.

Analysis of geographic data often requires matching GPS traces to road segments. Unfortunately, map data is often incomplete, resulting in failed or incorrect matches. In this paper, we extend an HMM map-matching algorithm to handle missing blocks. We test our algorithm using map data from the Cyclopath geowiki and GPS traces from Cyclopath's mobile app. Even for conservative cutoff distances, our algorithm found a significant amount of missing data per set of GPS traces. We tested the algorithm for accuracy by removing existing blocks from our map dataset. As the cutoff distance was lowered, false negatives were decreased from 34% to 16% as false positives increased from 5% to 10%. Although the algorithm degrades with increasing amounts of missing data, our results show that our extensions have the potential to improve both map matches and map data.

The home wardrobe is a complex and variable system, interacted with daily by its user/manager in a time- and resource-constrained decision-making process. Ubiquitous computing technology offers advantages in augmenting the decision-making process, and the potential to simultaneously encourage sustainable behaviors. In this study we present an empirical analysis of the contents of 11 home wardrobes and 3-6 months of daily dressing decisions for 5 users. We find that an average of only 7% of our female participants' wardrobes and 47% of our male participants' wardrobes are in regular use. In addition, we present an analysis of wardrobe contents, outfit composition, and garment utility in the wardrobe.

Online communities produce rich behavioral datasets, e.g., Usenet news conversations, Wikipedia edits, and Facebook friend networks. Analysis of such datasets yields important insights (like the "long tail" of user participation) and suggests novel design interventions (like targeting users with personalized opportunities and work requests). However, certain key user data typically are unavailable, specifically viewing, pre-registration, and non-logged-in activity. The absence of data makes some questions hard to answer; ac- cess to it can strengthen, extend, or cast doubt on previous results. We report on analysis of user behavior in Cyclopath, a geographic wiki and route-finder for bicyclists. With access to viewing and non-logged-in activity data, we were able to: (a) replicate and extend prior work on user lifecycles in Wikipedia, (b) bring to light some pre-registration activity, thus testing for the presence of "educational lurking," and (c) demonstrate the locality of geographic activity and how editing and viewing are geographically correlated.

Open content communities such as wikis derive their value from the work done by users. However, a key challenge is to elicit work that is sufficient and focused where needed. We address this challenge in a geographic open content commu-nity, the Cyclopath bicycle route finding system. We devised two techniques to elicit and focus user work, one using fa-miliarity to direct work opportunities and another visually highlighting them. We conducted a field experiment, find-ing that (a) the techniques succeeded in eliciting user work, (b) the distribution of work across users was highly unequal, and (c) user work benefitted the community (reducing the length of the average computed route by 1 kilometer). Author Keywords Wiki, geowiki, open content, geographic volunteer work, volunteered geographic information ACM Classification Keywords H.5.3 [Group and Organization Interfaces]: Collaborative computing, computer-supported cooperative work, web-based interaction.

We introduced tags into the Cyclopath geographic wiki for bicyclists. To promote the creation of useful tags, we made tags wiki objects, giving ownership of tag applications to the community, not to individuals. We also introduced a novel interface that lets users fine-tune their routing preferences with tags. We analyzed the Cyclopath tagging vocabulary, the relationship of tags to existing annotation techniques (notes and ratings), and the roles users take on with respect to tagging, notes, and ratings. Our findings are: two distinct tagging vocabularies have emerged, one around each of the two main types of geographic objects in Cyclopath; tags and notes have overlapping content but serve distinct purposes; users employ both ratings and tags to express their route-finding preferences, and use of the two techniques is moderately correlated; and users are highly specialized in their use of tags and notes. These findings suggest new design opportunities, including semi-automated methods to infer new annotations in a geographic context.

Many online mapping applications let users define routes, perhaps for sharing a favorite bicycle commuting route or rating several contiguous city blocks. At the UI level, defining a route amounts to selecting a fairly large number of objects - the individual segments of roads and trails that make up the route. We present a novel interaction technique for this task called path selection. We implemented the technique and evaluated it experimentally, finding that adding path selection to a state-of-the-art technique for selecting individual objects reduced route definition time by about a factor of 2, reduced errors, and improved user satisfaction. Detailed analysis of the results showed path selection is most advantageous (a) for routes with long straight segments and (b) when objects that are optimal click targets also are visually attractive.

Open content web sites depend on users to produce information of value. Wikipedia is the largest and most well-known such site. Previous work has shown that a small fraction of editors --Wikipedians -- do most of the work and produce most of the value. Other work has offered conjectures about how Wikipedians differ from other editors and how Wikipedians change over time. We quantify and test these conjectures. Our key findings include: Wikipedians' edits last longer; Wikipedians invoke community norms more often to justify their edits; on many dimensions of activity, Wikipedians start intensely, tail off a little, then maintain a relatively high level of activity over the course of their career. Finally, we show that the amount of work done by Wikipedians and non-Wikipedians differs significantly from their very first day. Our results suggest a design opportunity: customizing the initial user experience to improve retention and channel new users' intense energy.

Scientific peer review, open source software development, wikis, and other domains use distributed review to improve quality of created content by providing feedback to the work's creator. Distributed review is used to assess or improve the quality of a work (e.g., an article). However, it can also provide learning benefits to the participants in the review process. We developed an online review system for beginning computer programming students; it gathers multiple anonymous peer reviews to give students feedback on their programming work. We deployed the system in an introductory programming class and evaluated it in a controlled study. We find that: peer reviews are accurate compared to an accepted evaluation standard, that students prefer reviews from other students with less experience than themselves, and that participating in a peer review process results in better learning outcomes.

Google Maps and its spin-offs are highly successful, but they have a major limitation: users see only pictures of geographic data. These data are inaccessible except by limited vendor-defined APIs, and associated user data are weakly linked to them. But some applications require access, specifically geowikis and computational geowikis. We present the design and implementation of a computational geowiki. We also show empirically that both geowiki and computational geowiki features are necessary for a representative domain, bicycling, because (a) cyclists have useful knowledge unavailable except from cyclists and (b) cyclist-oriented automatic route-finding is enhanced by user input. Finally, we derive design implications: for example, user contributions presented within a route description are useful, and wikis should support contribution of opinion as well as fact.

Recommender systems try to address the "new user problem" by quickly and painlessly learning user preferences so that users can begin receiving recommendations as soon as possible. We take an expanded perspective on the new user experience, seeing it as an opportunity to elicit valuable contributions to the community and shape subsequent user behavior. We conducted a field experiment in MovieLens where we imposed additional work on new users: not only did they have to rate movies, they also had to enter varying numbers of tags. While requiring more work led to fewer users completing the entry process, the benefits were significant: the remaining users produced a large volume of tags initially, and continued to enter tags at a much higher rate than a control group. Further, their rating behavior was not depressed. Our results suggest that careful design of the initial user experience can lead to significant benefits for an online community.

With new technology, people can share information about everyday places they go; the resulting data helps others find and evaluate places. Recent applications like Dodgeball and Sharescape repurpose everyday place information: users create local place data for personal use, and the systems display it for public use. We explore both the opportunities -- new local knowledge, and concerns -- privacy risks, raised by this implicit information sharing. We conduct two empirical studies: subjects create place data when using PlaceMail, a location-based reminder system, and elect whether to share it on Sharescape, a community map-building system. We contribute by: (1) showing location-based reminders yield new local knowledge about a variety of places, (2) identifying heuristics people use when deciding what place-related information to share (and their prevalence), (3) detailing how these decision heuristics can inform local knowledge sharing system design, and (4) identifying new uses of shared place information, notably opportunistic errand planning.

Wikipedia’s brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that frequent editors dominate what people see when they visit Wikipedia, and that this domination is increasing. Similarly, using the same impact measure, we show that the probability of a typical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these findings.

Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

Many small online communities would benefit from increased diversity or activity in their membership. Some communities run the risk of dying out due to lack of participation. Others struggle to achieve the critical mass necessary for diverse and engaging conversation. But what tools are available to these communities to increase participation? Our goal in this research was to spark contributions to the movielens.org discussion forum, where only 2% of the members write posts. We developed personalized invitations, messages designed to entice users to visit or contribute to the forum. In two field experiments, we ask (1) if personalized invitations increase activity in a discussion forum, (2) how the choice of algorithm for intelligently choosing content to emphasize in the invitation affects participation, and (3) how the suggestion made to the user affects their willingness to act. We find that invitations lead to increased participation, as measured by levels of reading and posting. More surprisingly, we find that invitations emphasizing the social nature of the discussion forum increase user activity, while invitations emphasizing other details of the discussion are less successful.

The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, “home”, “work” or “Northwest Health Club”. It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices. Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than “points” are required to represent the full range of people's everyday places.

Many online communities are emerging that, like Wikipedia, bring people together to build community-maintained artifacts of lasting value (CALVs). Motivating people to contribute is a key problem because the quantity and quality of contributions ultimately determine a CALV's value. We pose two related research questions: 1) How does intelligent task routing---matching people with work---affect the quantity of contributions? 2) How does reviewing contributions before accepting them affect the quality of contributions? A field experiment with 197 contributors shows that simple, intelligent task routing algorithms have large effects. We also model the effect of reviewing contributions on the value of CALVs. The model predicts, and experimental data shows, that value grows more slowly with review before acceptance. It also predicts, surprisingly, that a CALV will reach the same final value whether contributions are reviewed before or after they are made available to the community.

Item-oriented Web sites maintain repositories of information about things such as books, games, or products. Many of these Web sites offer discussion forums. However, these forums are often disconnected from the rich data available in the item repositories. We describe a system, movie linking, that bridges a movie recommendation Web site and a movie-oriented discussion forum. Through automatic detection and an interactive component, the system recognizes references to movies in the forum and adds recommendation data to the forums and conversation threads to movie pages. An eight week observational study shows that the system was able to identify movie references with precision of .93 and recall of .78. Though users reported that the feature was useful, their behavior indicates that the feature was more successful at enriching the interface than at integrating the system.

Although they have potential, to date location-based information systems have not radically improved the way we interact with our surroundings. To study related issues, we developed a location-based reminder system, PlaceMail, and demonstrate its utility in supporting everyday tasks through a month-long field study. We identify current tools and practices people use to manage distributed tasks and note problems with current methods, including the common "to-do list". Our field study shows that PlaceMail supports useful location-based reminders and functional place-based lists. The study also sheds rich and surprising light on a new issue: when and where to deliver location-based information. The traditional 'geofence' radius around a place proves insufficient. Instead, effective delivery depends on people's movement patterns through an area and the geographic layout of the space. Our results both provide a compelling demonstration of the utility of location-based information and raise significant new challenges for location-based information distribution.

In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.

Online communities need regular maintenance activities such as moderation and data input, tasks that typically fall to community owners. Communities that allow all members to participate in maintenance tasks have the potential to be more robust and valuable. A key challenge in creating member-maintained communities is building interfaces, algorithms, and social structures that encourage people to provide high-quality contributions. We use Karau and Williams' collective effort model to predict how peer and expert editorial oversight affect members' contributions to a movie recommendation website and test these predictions in a field experiment with 87 contributors. Oversight increased both the quantity and quality of contributions while reducing antisocial behavior, and peers were as effective at oversight as experts. We draw design guidelines and suggest avenues for future work from our results.

Social matching systems bring people together in both physical and online spaces. They have the potential to increase social interaction and foster collaboration. However, social matching systems lack a clear intellectual foundation: the nature of the design space, the key research challenges, and the roster of appropriate methods are all ill-defined. This article begins to remedy the situation. It clarifies the scope of social matching systems by distinguishing them from other recommender systems and related systems and techniques. It identifies a set of issues that characterize the design space of social matching systems and shows how existing systems explore different points within the design space. It also reviews selected social science results that can provide input into system design. Most important, the article presents a research agenda organized around a set of claims. The claims embody our understanding of what issues are most important to investigate, our beliefs about what is most likely to be true, and our suggestions of specific research directions to pursue.

Online communities can help people form productive relationships. Unfortunately, this potential is not always fulfilled: many communities fail, and designers don't have a solid understanding of why. We know community activity begets activity. The trick, however, is to inspire participation in the first place. Social theories suggest methods to spark positive community participation. We carried out a field experiment that tested two such theories. We formed discussion communities around an existing movie recommendation web site, manipulating two factors: (1) similarity-we controlled how similar group members' movie ratings were; and (2) uniqueness-we told members how their movie ratings (with respect to a discussion topic) were unique within the group. Both factors positively influenced participation. The results offer a practical success story in applying social science theory to the design of online communities.

We present a conceptual framework for location-aware community systems and results from two studies of how socially-defined places influence people's information sharing and communication needs.

Modern work is a highly social process, offering many cues for people to organize communication and access information. Shared physical workplaces provide natural support for tasks such as (a) social reminding about communication commitments and keeping track of collaborators and friends, and (b) social data mining of local expertise for advice and information. However, many people now collaborate remotely using tools such as email and voicemail. Our field studies show that these tools do not provide the social cues needed for group work processes. In part, this is because the tools are organized around messages, rather than people. In response to this problem, we created ContactMap, a system that makes people the primary unit of interaction. ContactMap provides a structured social desktop representation of users' important contacts that directly supports social reminding and social data mining. We conducted an empirical evaluation of ContactMap, comparing it with traditional email systems, on tasks suggested by our fieldwork. Users performed better with ContactMap and preferred ContactMap for the majority of these tasks. We discuss future enhancements of our system and the implications of these results for future communication interfaces and for theories of mediated communication.

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

Although the Internet provides powerful tools for social interactions, many tasks-for example, information-seeking-are undertaken as solitary activities. Information seekers are unaware of the invisible crowd traveling in parallel to their course through the information landscape. Social navigation systems attempt to make the invisible crowd visible, while social recommender systems try to introduce people directly. However, it is not clear whether users desire or will respond to social cues indicating the presence of other people when they are focused on a task. To investigate this issue, we created an online game-playing task and paired subjects to perform the task based on their responses to a short survey about demographics and interests. We studied how these factors influence task outcomes, the interaction process, and attitudes towards one's partner. We found that demographic similarity affected how people interact with each other, even though this information was not explicit, while similarities or differences in task-relevant interests did not. Our findings suggest guidelines for developing social recommender systems and show the need for further research into conditions that will help such systems succeed.

Social data mining systemsenable people to share opinions and benefit from each other's experience. They do this by mining and redistributing information from computational records of social activity such as Usenet messages, system usage history, citations, or hyperlinks. Some general questions for evaluating such systems are: (1) is the extracted information valuable? and (2) do interfaces based on the information improve user task performance? We report here onTopicShop, a system that mines information from the structure and content of Web pages and provides an exploratory information workspace interface. We carried out experiments that yielded positive answers to both evaluation questions. First, a number of automatically computable features about Web sites do a good job of predicting expert quality judgments about sites. Second, compared to popular Web search interfaces, the TopicShop interface to this information lets users select significantly more high-quality sites, in less time and with less effort, and to organize the sites they select into personally meaningful collections more quickly and easily. We conclude by discussing how our results may be applied and considering how they touch on general issues concerning quality, expertise, and consensus.

Many applications require users to specify preferences. We support users in this task by letting them define preferences relative to their personal history or that of other users. We implement this idea using a graphical technique called control shadows, which we have implemented on both a desktop computer and on a cell phone with a small, grayscale display. An empirical study compared user performance on the graphical interface and a text table interface with identical functionality. On the desktop, users completed their tasks more quickly and effectively and strongly preferred the graphical interface. On the cell phone, there was no significant difference between the graphical and table interfaces. Finally, personal history proved useful in specifying preferences, but history of other users was not helpful

Much of our daily communication activity involves managing interpersonal communications and relationships. Despite its importance, this activity of contact management is poorly understood. We report on field and lab studies that begin to illuminate it.A field study of business professionals confirmed the importance of contact management and revealed a major difficulty: selecting important contacts from the large set of people with whom one communicates. These interviews also showed that communication history is a key resource for this task. Informants identified several history-based criteria that they considered useful.We conducted a lab study to test how well these criteria predict contact importance. Subjects identified important contacts from their email archives. We then analyzed their email to extract features for all contacts. Reciprocity, recency and longevity of email interaction proved to be strong predictors of contact importance. The experiment also identified another contact management problem: removing 'stale' contacts from long term archives. We discuss the design and theoretical implications of these results.

For many topics, the World Wide Web contains hundreds or thousands of relevant documents of widely varying quality. Users face a daunting challenge in identifying a small subset of documents worthy of their attention.

Article TopicShop: enhanced support for evaluating and organizing collections of Web sites Share on Authors: Brian Amento AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ and Department of Computer Science, Virginia Tech, Blacksburg, VA AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ and Department of Computer Science, Virginia Tech, Blacksburg, VAView Profile , Loren Terveen AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJView Profile , Will Hill AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJView Profile , Deborah Hix Department of Computer Science, Virginia Tech, Blacksburg, VA Department of Computer Science, Virginia Tech, Blacksburg, VAView Profile Authors Info & Claims UIST '00: Proceedings of the 13th annual ACM symposium on User interface software and technologyNovember 2000 Pages 201–209https://doi.org/10.1145/354401.354771Online:01 November 2000Publication History 19citation728DownloadsMetricsTotal Citations19Total Downloads728Last 12 Months21Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We identify a problem with the process of research in the human-computer interaction (HCI) community-an overemphasis on "radical invention" at the price of achieving a common research focus. Without such a focus, it is difficult to build on previous work, to compare different interaction techniques objectively, and to make progress in developing theory. These problems at the research level have implications for practice, too; as researchers we often are unable to give principled design advice to builders of new systems. We propose that the HCI community try to achieve a common focus around the notion of reference tasks. We offer arguments for the advantages of this approach as well as consider potential difficulties. We explain how reference tasks have been highly effective in focusing research into information retrieval and speech recognition. We discuss what factors have to be considered in selecting HCI reference tasks and present an example reference task (for searching speech archives). This example illustrates the nature of reference tasks and points to the issues and problems involved in constructing and using them. We conclude with recommendations about what steps need to be taken to execute the reference task research agenda. This involves recommendations about both the technical research that needs to be done and changes in the way that the HCI research community operates. The technical research involves identification of important user tasks by systematic requirements gathering, definition and operationalization of reference tasks and evaluation metrics, and execution of task-based evaluation, along with judicious use of field trials. Perhaps more important, we have also suggested changes in community practice that HCI must adopt to make the reference tasks idea work. We must create forums for discussion of common tasks and methods by which people can compare systems and techniques. Only by doing this can the notion of reference tasks be integrated into the process of research and development, enabling the field to achieve the focus it desperately needs.

Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created the TopicShop system to address this need. TopicShop includes (1) a webcrawler that discovers relevant web sites and builds site profiles, and (2) user interfaces for exploring and organizing sites. We conducted an empirical study comparing user performance with TopicShop vs. YahooTM. TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 8 1% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provides - in particular, the number of pages on a site and the number of other sites that link to it - was the key to these results, as users exploited it to identify the most promising sites quickly and easily.

For many purposes, the Web page is too small a unit of interaction and analysis. Web sites are structured multimedia documents consisting of many pages, and users often are interested in obtaining and evaluating entire collections of topically related sites. Once such a collection is obtained, users face the challenge of exploring, comprehending and organizing the items. We report four innovations that address these user needs: (1) we replaced the Web page with the Websiteas the basic unit of interaction and analysis;(2) we defined a new informationstructure, theclan graph, that groups together sets of related sites; (3) we augment the representation of a site with asite profile, information about site structure and content that helps inform user evaluation of a site; and (4) we invented a new graph visualization, theauditorium visualization, that reveals important structural and content properties of sites within a clan graph. Detailed analysis and user studies document the utility of this approach. The clan graph construction algorithm tends to filter out irrelevant sites and discover additional relevant items. The auditorium visualization, augmented with drill-down capabilities to explore site profile data, helps users to find high-quality sites as well as sites that serve a particular function.

For many purposes, the Web page is too small a unit of interaction.Users often want to interact with larger-scale entities, particularly collections of topically related items.We report three innovations that address this user need.l We replaced the web page with the web sire as the basic unit of interaction and analysis-* We defined a new information structure, the clan graph, that groups together sets of related sites.lWe invented a new graph visualization, the auditorium visualization, that reveals important structural and content properties of sites within a clan graph.We have discovered interesting information that can be extracted from the structure of a clan graph.We can identify structurally important sites with many incoming or outgoing links.Lii between sites serve important functions: they often identify "f?ont door" pages of sites, sometimes identify especially significant pages within a site, and occasionally contain informative anchor text.

Links between web sites can be seen as evidence of a type of emergent collaboration among web site authors. We report here on an empirical investigation into emergent collaboration. We developed a webcrawling algorithm and tested its performance on topics volunteered by 30 subjects. Our findings include: . Some topics exhibit emergent collaboration, some do not. The presence of commercial sites reduces collaboration. . When sites are linked with other sites, they tend to group into one large, tightly connected component. . Connectivity can serve as the basis for collaborative filtering. Human experts rate connected sites as significantly more relevant and of higher quality. Keywords Social filtering, collaborative filtering, computer supported cooperative work, human computer interaction, information access, information retrieval INTRODUCTION The field of CSCW sees collaboration as involving people who know they are working together, e.g., to edit a document, to carry out a soft...

Usenet may be regarded as the worl$s largest conversatioti appficatioq with over 17,000 newsgronps and 3 fion users.Despite its ubiquity and poptiari~, however, we know Etie about the natore of the interactions it suppo~.~s empirical paper investigates mass interaction in UseneL We anrdyse over 2.15 Mon messages from 659,450 posters, co~ected from 500 newsgoups over 6 months.We fit characterise mass interactio~presenting basic data about demographics, conversational strategies and interactivity-Using predictions tiom the common ground [3] model of interactio~we next conduct causal modetig to determine relations bemeen demographics, conversational strategim and interactiv~.We find evidence for moderate conversatiomd threading, but hge participation ine@ties in Usene4 with a d minority of participants posting a large proportion of messages.Contrary to the common gromd model and 'Etiquette" guidefies [8,10] we ho find that "cross-posting" to extemd newsgroups is hig~y fiequenk Our predictions about the eEects of demographics on conversatioti strategy were largely cotie~but we found disconfirming evidence about the relations befi'een conversational strategy and interactivity.Contrary to our expectations, boti cross-posting and short messages promote interactivity.We conclude that in order to e~lain mass interaction, the common ground model must be mowed to ticorporate notions of weak ties [5,q and communication overload [11,18].Ke~ords hlass interactio~Usene< conversatio~newsgroups, common groun~moderatio~FAQS, netiquette, empirid. lNTRODUC~ONUsenet maybe regarded as the worl&s largest and fmtest growing conversationrd appficatiom k 1988 there were fewer than 500 news~oups.bent estimatesvary, but at the time of our dab co~ection in Dec. 1996, there were Ptission 10de distal or hard copies ofall or pti ofthis ~vorkfor personal or classroom use is granted without f= protided that copies Zrenotmade or dis~%uted for profit or commercial advantage and that copies bear this notice and the fill citation on the fimt paga To copy othm~ise to republish, to post on sen'em or to redistribute to Iis& Tquires prior sptific permission and~ora fee CSCW 98 Seatie Wastinson USA ---_ -.

As people participate in the thousands of global conversations that comprise Usenet news, one thing they do is post their opinions of web resources. Phoaks is a collaborative filtering system that continuously parses, classifies, abstracts and tallies those opinions. About 3,500 users per day consult Phoaks web pages that reflect the results. Phoaks also features a general architecture for building similar collaborative filtering interfaces to conversational data. We report here on the Phoaks resource recommendation interface, the architecture, and the issues and experience that make up its rationale. Keywords human-computer interaction, human interface, computersupported cooperative work, organizational computing, social filtering, collaborative filtering, data mining,

Software agents are computer programs that act on behalf of users to perform routine, tedious, and time-consuming tasks.To be useful to an individual user, an agent must be personalized to his or her goals, habits, and preferences.We have created an end-user programming system that makes it easy for users to state rules for their agents to follow.The main advance over previous approaches is that the system automatically determines conflicts between rules and guides users in resolving the conflicts.Thus, user and system collaborate in developing and managing a set of rules that embody the user's preferences for handling a wide variety of situations.

We report on an investigation of using Usenet newsgroups for social filtering of Web resources. Our main empirical results are: (1) for the period of May ’96 to Jul ’96, about 23% of Usenet news messages mention Web resources, (2) 19% of resource mentions are recommendations (as opposed, e.g., to home pages), (3) we can’automatically recognize recommendations with at least 90% accuracy, and (4) in some newsgroups, certain resources are mentioned significantly more frequently than others and thus appear to play a central role for that community. We have created a Web site that summarizes the most frequently and recently mentioned Web resources for 1400 newsgroups.

We identify an important type of software design knowledge that we call community-specific folklore and discuss problems with current approaches to managing it. We developed a general framework for a living design memory, built a design memory tool, and deployed the tool in a large software development organization. The tool effectively disseminates knowledge relevant to local software design practice. It is embedded in the organizational process to help ensure that its knowledge evolves as necessary. This work illustrates important lessons in building knowledge management systems, integrating novel technology into organizational practice, and carrying out research-development partnerships.

I describe the IMA CS interface, which supports a type of interactive data exploration task called data archaeology.The interface facilitates users in performing this task using three key design principles:(1) combine power and ease of use, (2) provide direct support for integrated, itemtive data exploration, and (3) assist users in managing their work over time.I show how these principles are relevant in the data archaeology task, describe how knowledge representation technologyprovides a foundation for an adequate support system, and illustmte in detail how the interface offers powerful support for data archaeology.

Corporate databases increasingly are being viewed as potentially rich sources of new and valuable knowledge. Various approaches to “discovering” or “mining” such knowledge have been proposed. Here we identify an important and previously ignored discovery task, which we call data archaeology. Data archaeology is a skilled human task, in which the knowledge sought depends on the goals of the analyst, cannot be specified in advance, and emerges only through an iterative process of data segmentation and analysis. We describe a system that supports the data archaeologist with a natural, object-oriented representation of an application domain, a powerful query language and database translation routines, and an easy-to-use and flexible user interface that supports interactive exploration. A formal knowledge representation system provides the core technology that facilitates database integration, querying, and the reuse of queries and query results.