As LLMs gain adoption in high-stakes domains like mental health, domain experts are increasingly consulted to provide input into policies governing their behavior. From an observation of 19 policymaking workshops with 9 experts over 15 weeks, we identified opportunities to better support rapid experimentation, feedback, and iteration for collaborative policy design processes. We present PolicyPad, an interactive system that facilitates the emerging practice of LLM policy prototyping by drawing from established UX prototyping practices, including heuristic evaluation and storyboarding. Using PolicyPad, policy designers can collaborate on drafting a policy in real time while independently testing policy-informed model behavior with usage scenarios. We evaluate PolicyPad through workshops with 8 groups of 22 domain experts in mental health and law, finding that PolicyPad enhanced collaborative dynamics during policy design, enabled tight feedback loops, and led to novel policy contributions. Overall, our work paves expert-informed paths for advancing AI alignment and safety.

As AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel interaction designs that enable deeper human-agent collaboration. However, most prior works leverage human interaction to fix “autonomous” workflows that have yet to become fully autonomous or rigidly treat planning and execution as separate stages. Based on a formative study with 9 researchers using AI to support their work, we propose a design that affords greater flexibility in collaboration, so that users can 1) delegate agency to the user or agent via a collaborative plan where individual steps can be assigned; and 2) interleave planning and execution so that plans can adjust after partial execution. We introduce Cocoa, a system that takes design inspiration from computational notebooks to support complex research tasks. A lab study (n = 16) found that Cocoa enabled steerability without sacrificing ease-of-use, and a week-long field deployment (n = 7) showed how researchers collaborated with Cocoa to accomplish real-world tasks.

AI agents, or bots, serve important roles in online communities. However, they are often designed by outsiders or a few tech-savvy members, leading to bots that may not align with the broader community’s needs. How might communities collectively shape the behavior of community bots? We present Botender, a system that enables communities to collaboratively design LLM-powered bots without coding. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors tailored to community needs. Botender facilitates testing and iteration on bot behavior through case-based provocations: interaction scenarios generated to spark user reflection and discussion around desirable bot behavior. A validation study found these provocations more useful than standard test cases for revealing improvement opportunities and surfacing disagreements. During a five-day deployment across six Discord servers, Botender supported communities in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.

While LLMs now enable social media users to create content classifiers easily through natural language, automatic prompt optimization techniques are often necessary to create performant classifiers. However, such techniques can fail to consider how users want to evolve their classifiers over the course of usage, including desiring to steer them in different ways during initialization and refinement. We introduce a user-centered prompt optimization technique, Promptimizer, that maintains high performance and ease-of-use but additionally (1) allows for user input into the optimization process and (2) produces final prompts that are interpretable. A lab experiment (n=16) found that users significantly preferred Promptimizer’s human-in-the-loop optimization over a fully automatic approach. We also implement Promptimizer into Puffin, a tool to support YouTube content creators in creating and maintaining personal classifiers to manage their comments. Over a 3-week deployment with 10 creators, participants successfully created diverse filters to better understand their audiences and protect their communities.

Creative writing is a deeply human craft, yet AI systems using large language models (LLMs) offer the automation of significant parts of the writing process.So why do some creative writers choose to use AI? Through interviews and observed writing sessions with 18 creative writers who already use AI regularly in their writing practice, we find that creative writers are intentional about how they incorporate AI, making many deliberate decisions about when and how to engage AI based on their core values, such as authenticity and craftsmanship.We characterize the interplay between writers' values, their fluid relationships with AI, and specific integration strategies-ultimately enabling writers to create new AI workflows without compromising their creative values.We provide insight for writing communities, AI developers and future researchers on the importance of supporting transparency of these emerging writing processes and rethinking what AI features can best serve writers.

Most vision-language models today are primarily trained on English image-text pairs, with non-English pairs often filtered out. Evidence from cross-cultural psychology suggests that this approach will bias models against perceptual modes exhibited by people who speak other (non-English) languages. We investigate semantic and expressive variation in image captions across different languages; we analyze both human-annotated datasets and model-produced captions. By analyzing captions across seven languages (English, French, German, Russian, Chinese, Japanese, Korean) in high-quality image captioning datasets (Crossmodal and Visual Genome), we find that multilingual caption sets tend to provide richer visual descriptions than monolingual (including English-only) ones; multilingual sets contain 46.0% more objects, 66.1% more relationships, and 66.8% more attributes. We observe the same results with multilingual captions produced by LLaVA and the Google Vertex API: for example, compared to monolingual captions, they cover 21.9% more objects, 18.8% more relations, and 20.1% more attributes. These suggest that, across a large number of samples, different languages bias people and models to focus on different visual concepts. Finally, we show that models trained on image-text data in one language perform distinctly better on that language’s test set. Our work points towards the potential value of training vision models on multilingual data sources to widen the range/variation of descriptive information those models are exposed to.

Rules are a critical component of the functioning of nearly every online community, yet it is challenging for community moderators to make data-driven decisions about what rules to set for their communities. The connection between a community's rules and how its membership feels about its governance is not well understood. In this work, we conduct the largest-to-date analysis of rules on Reddit, collecting a set of 67,545 unique rules across 5,225 communities which collectively account for more than 67% of all content on Reddit. More than just a point-in-time study, our work measures how communities change their rules over a 5+ year period. We develop a method to classify these rules using a taxonomy of 17 key attributes extended from previous work. We assess what types of rules are most prevalent, how rules are phrased, and how they vary across communities of different types. Using a dataset of communities' discussions about their governance, we are the first to identify the rules most strongly associated with positive community perceptions of governance: rules addressing who participates, how content is formatted and tagged, and rules about commercial activities. We conduct a longitudinal study to quantify the impact of adding new rules to communities, finding that after a rule is added, community perceptions of governance immediately improve, yet this effect diminishes after six months. Our results have important implications for platforms, moderators, and researchers. We make our classification model and rules datasets public to support future research on this topic.

Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumptions are perceived by researchers seeking to study and design social computing systems for neurodivergent people. From a critical analysis of 84 publications systematically gathered across a decade of social computing research, we determine that research into social computing with neurodiverse participants is largely medicalized, adheres to historical stereotypes of neurodivergent children and their families, and is insensitive to the wide spectrum of neurodivergent people that are potential users of social technologies. When social computing systems designed for neurodivergent people rely upon a conception of disability that restricts expression for the sake of preserving existing norms surrounding social experience, the result is often simplistic and restrictive systems that prevent users from "being social" in a way that feels natural and enjoyable. We argue that a neurodiversity perspective informed by critical disability theory allows us to engage with alternative forms of sociality as meaningful and desirable rather than a deficit to be compensated for. We conclude by identifying opportunities for researchers to collaborate with neurodivergent users and their communities, including the creation of spectrum-conscious social systems and the embedding of double empathy into systems for more equitable design.

As AI tools become increasingly integrated into cognitively demanding tasks, like note-taking, questions remain about whether they enhance or compromise cognitive engagement. This paper examines the ''AI Assistance Dilemma'' in note-taking, investigating how varying levels of AI support affect user engagement and comprehension. In a within-subject experiment, we asked participants (N=30) to take notes during lecture videos under three conditions: Automated AI (high assistance with structured notes), Intermediate AI (moderate assistance with real-time summary, and Minimal AI (low assistance with transcript). Results reveal that Intermediate AI yields the highest post-test scores and Automated AI the lowest. Participants, however, preferred the automated setup due to its perceived ease of use and lower cognitive effort, suggesting a discrepancy between preferred convenience and cognitive benefits. Our study provides insights into designing AI assistance that preserves cognitive engagement, offering implications for designing moderate AI support in cognitive tasks.

Social Virtual Reality (VR) games offer immersive socialization experiences but pose significant challenges of harassment. Common solutions, such as reporting and moderation, address harassment after it happens but fail to prevent or stop harassment in the moment. In this study, we explore and design proactive and instant-reactive safety designs to mitigate harassment in social VR. Proactive designs prevent harassment from occurring, while instant-reactive designs minimize harm during incidents. We explore three directions for design: user-initiated personal bubbles, clarifying social norms, and encouraging bystander intervention. Through an iterative process, we first conducted a formative interview study to determine design goals for making these features effective, fit user needs, and robust to manipulation. We then implemented Puffer, an integrated safety system that includes a suite of proactive and instant-reactive features, as a social VR prototype. From an evaluation using simulated scenarios with participants, we find evidence that Puffer can help protect players during emergencies, foster prosocial norms, and create more positive social interactions. We conclude by discussing how system safety features can be designed to complement existing proactive and instant-reactive strategies, particularly for people with marginalized identities.

Millions of online communities are governed by volunteer moderators, who shape their communities by setting and enforcing rules, recruiting additional moderators, and participating in the community themselves. These moderators must regularly make decisions about how to govern, yet measuring the 'success' of governance is complex and nuanced, making it challenging to determine what governance strategies are most successful. Furthermore, prior work has shown that communities have differing values, suggesting that 'one-size-fits-all' approaches to governance are unlikely to serve all communities well. In this work, we assess governance practices on reddit by classifying the sentiment of community members' public discussion of their own moderators. We label 1.89 million posts and comments made on reddit over an 18 month period. We relate these perceptions to characteristics of community governance, and to different actions that community moderators take. We identify types of communities where moderators are perceived particularly positively and negatively, and highlight promising strategies for moderator teams. Amongst other findings, we show that strict rule enforcement is linked to more favorable perceptions of moderators of communities dedicated to certain topics, such as news communities, than others. We investigate what kinds of moderators are associated with improved community perceptions upon their addition to a mod team, and find that moderators who are active community members before and during their mod tenures are seen more favorably. We make our models, anonymized datasets, and code public.

Responding to misinformation online can be an exhausting and thankless task. It takes time and energy to write effective content, puts users at risk of online harassment, and strains personal relationships. Despite these challenges, there are people who voluntarily respond to misinformation online, and some have established communities on platforms such as Reddit, Discord, and X (formerly Twitter) dedicated to these efforts. In this work, we interviewed 8 people who participate in such communities to understand the type of support they receive from each other in these discussion spaces. Interviewees described that their communities helped them sustain motivation, save time, and improve their communication skills. Common practices included sharing sources and citations, providing emotional support, giving others advice, and signaling positive feedback. We present our findings as three case studies and discuss opportunities for future work to support collaborative practices in online communities dedicated to misinformation response. Our work surfaces how resource sharing, social motivation, and decentralization can make misinformation correction more sustainable, rewarding, and effective for online citizens.

Online communities can offer many benefits for youth including peer learning, cultural expression, and skill development. However, most HCI research on youth-focused online communities has centered communities developed by adults for youth rather than by the youth themselves. In this work, we interviewed 11 teenagers (ages 13-17) who moderate online Discord communities created by youth, for youth. Participants were identified by Discord platform staff as leaders of well-moderated servers through an intensive exam and application-based process. We also interviewed 2 young adults who volunteered as mentors of some of our teen participants. We present our findings about the benefits, motivations, and risks of teen-led online communities, as well as the role of external stakeholders of these youth spaces. We contextualize our work within the broader teen online safety landscape to provide recommendations to better support, encourage, and protect teen moderators and their online communities. This empirical work contributes one of the first studies to date with teen Discord moderators and aims to empower safe youth-led online communities.

Social media feeds are deeply personal spaces that reflect individual values and preferences. However, top-down, platform-wide content algorithms can reduce users’ sense of agency and fail to account for nuanced experiences and values. Drawing on the paradigm of interactive machine teaching (IMT), an interaction framework for non-expert algorithmic adaptation, we map out a design space for teachable social media feed experiences to empower agential, personalized feed curation. To do so, we conducted a think-aloud study (N = 24) featuring four social media platforms—Instagram, Mastodon, TikTok, and Twitter—to understand key signals users leveraged to determine the value of a post in their feed. We synthesized users’ signals into taxonomies that, when combined with user interviews, inform five design principles that extend IMT into the social media setting. We finally embodied our principles into three feed designs that we present as sensitizing concepts for teachable feed experiences moving forward.

As more users turn to video-sharing platforms like YouTube as an information source, they may consume misinformation despite their best efforts. In this work, we investigate ways that users can better assess the credibility of videos by first exploring how users currently determine credibility using existing signals on platforms and then by introducing and evaluating new credibility-based signals. We conducted 12 contextual inquiry interviews with YouTube users, determining that participants used a combination of existing signals, such as the channel name, the production quality, and prior knowledge, to evaluate credibility, yet sometimes stumbled in their efforts to do so. We then developed Viblio, a prototype system that enables YouTube users to view and add citations and related information while watching a video based on our participants’ needs. From an evaluation with 12 people, all participants found Viblio to be intuitive and useful in the process of evaluating a video’s credibility and could see themselves using Viblio in the future.

In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask “newbie” questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.

Internet users have formed a wide array of online communities with diverse community goals and nuanced norms. However, most online platforms only offer a limited set of governance models in their software infrastructure and leave little room for customization. Consequently, technical proficiency becomes a prerequisite for online communities to build governance policies in code, excluding non-programmers from participation in designing community governance. In this paper, we present Pika, a system that empowers non-programmers to author a wide range of executable governance policies. At its core, Pika incorporates a declarative language that decomposes governance policies into modular components, thereby facilitating expressive policy authoring through a user-friendly, form-based web interface. Our user studies with 10 non-programmers and 7 programmers show that Pika can empower non-programmers to author policies approximately 2.5 times faster than programmers who author in code. We also provide insights about Pika’s expressivity in supporting diverse policies online communities want.

Many researchers studying online communities seek to make them better. However, beyond a small set of widely-held values, such as combating misinformation and abuse, determining what `better’ means can be challenging, as community members may disagree, values may be in conflict, and different communities may have differing preferences as a whole. In this work, we present the first study that elicits values directly from members across a diverse set of communities. We survey 212 members of 627 unique subreddits and ask them to describe their values for their communities in their own words. Through iterative categorization of 1,481 responses, we develop and validate a comprehensive taxonomy of community values, consisting of 29 subcategories within nine top-level categories enabling principled, quantitative study of community values by researchers. Using our taxonomy, we reframe existing research problems, such as managing influxes of new members, as tensions between different values, and we identify understudied values, such as those regarding content quality and community size. We call for greater attention to vulnerable community members' values, and we make our codebook public for use in future research.

Social computing is the study of how technology shapes human social interactions. This topic has become increasingly relevant to secondary school students (ages 11--18) as more of young people's everyday social experiences take place online, particularly with the continuing effects of the COVID-19 pandemic. However, social computing topics are rarely touched upon in existing middle and high school curricula. We seek to introduce concepts from social computing to secondary school students so they can understand how computing has wide-ranging social implications that touch upon their everyday lives, as well as think critically about both the positive and negative sides of different social technology designs. In this report, we present a series of six lessons combining presentations and hands-on activities covering topics within social computing and detail our experience teaching these lessons to approximately 1,405 students across 13 middle and high schools in our local school district. We developed lessons covering how social computing relates to the topics of Data Management, Encrypted Messaging, Human-Computer Interaction Careers, Machine Learning and Bias, Misinformation, and Online Behavior. We found that 81.13% of students expressed greater interest in the content of our lessons compared to their interest in STEM overall. We also found from pre- and post-lesson comprehension questions that 63.65% learned new concepts from the main activity. We release all lesson materials on a website for public use. From our experience, we observed that students were engaged in these topics and found enjoyment in finding connections between computing and their own lives.

The increasing harms caused by hate, harassment, and other forms of abuse online have motivated major platforms to explore hierarchical governance. The idea is to allow communities to have designated members take on moderation and leadership duties; meanwhile, members can still escalate issues to the platform. But these promising approaches have only been explored in plaintext settings where community content is public to the platform. It is unclear how one can realize hierarchical governance in the huge and increasing number of online communities that utilize end-to-end encrypted (E2EE) messaging for privacy.We propose private hierarchical governance systems. These should enable similar levels of community governance as in plaintext settings, while maintaining cryptographic privacy of content and governance actions not reported to the platform. We design the first such system, taking a layered approach that adds governance logic on top of an encrypted messaging protocol; we show how an extension to the message layer security (MLS) protocol suffices for achieving a rich set of governance policies. Our approach allows developers to rapidly prototype new governance features, taking inspiration from a plaintext system called PolicyKit. We build a prototype E2EE messaging system called MlsGov that supports content-based community and platform moderation, elections of community moderators, votes to remove abusive users, and more.

Navigating the vast scientific literature often starts with browsing a paper’s abstract. However, when a reader seeks additional information, not present in the abstract, they face a costly cognitive chasm during their dive into the full text. To bridge this gap, we introduce recursively expandable abstracts, a novel interaction paradigm that dynamically expands abstracts by progressively incorporating additional information from the papers’ full text. This lightweight interaction allows scholars to specify their information needs by quickly brushing over the abstract or selecting AI-suggested expandable entities. Relevant information is synthesized using a retrieval-augmented generation approach, presented as a fluid, threaded expansion of the abstract, and made efficiently verifiable via attribution to relevant source-passages in the paper. Through a series of user studies, we demonstrate the utility of recursively expandable abstracts and identify future opportunities to support low-effort and just-in-time exploration of long-form information contexts through LLM-powered interactions.

Relationships are perhaps the single greatest source of human happiness, and as part of building strong relationships, conflict and hard conversations are unavoidable. As people increasingly rely on digital communication to initiate and resolve conflicts, we examine how design can improve the experience of working through hard conversations within close relationships. We interviewed six psychotherapists and twenty-one social media users to understand both theoretical best practices for navigating conflict and users' experiences with hard conversations online, particularly on text-based messaging platforms. We used our findings to create a temporal model of how digital design could intervene to support users and their relationships during these conversations. Specifically, we find that design can help to facilitate more mutually consensual difficult conversations, support emotional regulation during the conversation, and help facilitate pauses when necessary. We explore the tensions between balancing the needs of relationships and the individuals in them in digital design, and how to center relationships in digital design.

In this work, we examine how fact-checkers prioritize which claims to fact-check and what tools may assist them in their efforts. Through a series of interviews with 23 professional fact-checkers from around the world, we validate that harm assessment is a central component of how fact-checkers triage their work. We also clarify the processes behind fact-checking prioritization, finding that they are typically ad hoc, and gather suggestions for tools that could help with these processes. To address the needs articulated by fact-checkers, we present a structured framework of questions to help fact-checkers negotiate the priority of claims through assessing potential harms. Our FABLE Framework of Misinformation Harms incorporates five dimensions of magnitude---(social) Fragmentation, Actionability, Believability, Likelihood of spread, and Exploitativeness---that can help determine the potential urgency of a specific message or claim when considering misinformation as harm. The result is a practical and conceptual tool to support fact-checkers and others as they make strategic decisions to prioritize their efforts. We conclude with a discussion of computational approaches to support structured prioritization, as well as applications beyond fact-checking to content moderation and curation.

A recent surge in remote meetings has led to complaints of Zoom fatigue" and collaboration overload," negatively impacting worker productivity and well-being. One way to alleviate the burden of meetings is to de-emphasize their synchronous participation by shifting work to and enabling sensemaking during post-meeting asynchronous activities. Towards this goal, we propose the design concept of meeting bridges, or information artifacts that can encapsulate meeting information towards bridging to and facilitating post-meeting activities. Through 13 interviews and a survey of 198 information workers, we learn how people use online meeting information after meetings are over, finding five main uses: as an archive, as task reminders, to onboard or support inclusion, for group sensemaking, and as a launching point for follow-on collaboration. However, we also find that current common meeting artifacts, such as notes and recordings, present challenges in serving as meeting bridges. After conducting co-design sessions with 16 participants, we distill key principles for the design of meeting bridges to optimally support asynchronous collaboration goals. Overall, our findings point to the opportunity of designing information artifacts that not only support users to access but also continue to transform and engage in meeting information post-meeting.

Social media systems are as varied as they are pervasive. They have been almost universally adopted for a broad range of purposes including work, entertainment, activism, and decision making. As a result, they have also diversified, with many distinct designs differing in content type, organization, delivery mechanism, access control, and many other dimensions. In this work, we aim to characterize and then distill a concise design space of social media systems that can help us understand similarities and differences, recognize potential consequences of design choice, and identify spaces for innovation. Our model, which we call Form-From, characterizes social media based on (1) the form of the content, either threaded or flat, and (2) from where or from whom one might receive content, ranging from spaces to networks to the commons. We derive Form-From inductively from a larger set of 62 dimensions organized into 10 categories. To demonstrate the utility of our model, we trace the history of social media systems as they traverse the Form-From space over time, and we identify common design patterns within cells of the model.

Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy, and law. This article is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. We collect a set of values that seem most relevant to recommender systems operating across different domains, and then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.

When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during literature reviews. This paper introduces CiteSee, a paper reading tool that leverages a user’s publishing, reading, and saving activities to provide personalized visual augmentations and context around citations. First, CiteSee connects the current paper to familiar contexts by surfacing known citations a user had cited or opened. Second, CiteSee helps users prioritize their exploration by highlighting relevant but unknown citations based on saving and reading history. We conducted a lab study that suggests CiteSee is significantly more effective for paper discovery than three baselines. A field deployment study shows CiteSee helps participants keep track of their explorations and leads to better situational awareness and increased paper discovery via inline citation when conducting real-world literature reviews.

User experience (UX) has undergone a revolution in collaborative practices, due to tools that enable quick feedback and continuous collaboration with a varied team across a design’s lifecycle. However, it is unclear how this shift in collaboration has been received in professional UX practice, and whether new pain points have arisen. To this end, we conducted a survey (N = 114) with UX practitioners at software organizations based in the U.S. to better understand their collaborative practices and tools used throughout the design process. We found that while an increase in collaborative activity enhanced many aspects of UX work, some long-standing challenges—such as handing off designs to developers—still persist. Moreover, we observed new challenges emerging from activities enabled by collaborative tools such as design system management. Based on our findings, we discuss how UX practices can improve collaboration moving forward and provide concrete design implications for collaborative UX tools.

Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers’ related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list.

Open-source Software (OSS) has become a valuable resource in both industry and academia over the last few decades. Despite the innovative structures they develop to support the projects, OSS projects and their communities have complex needs and face risks such as getting abandoned. To manage the internal social dynamics and community evolution, OSS developer communities have started relying on written governance documents that assign roles and responsibilities to different community actors.To facilitate the study of the impact and effectiveness of formal governance documents on OSS projects and communities, we present a longitudinal dataset of 710 GitHub-hosted OSS projects with GOVERNANCE.MD governance files. This dataset includes all commits made to the repository, all issues and comments created on GitHub, and all revisions made to the governance file. We hope its availability will foster more research interest in studying how OSS communities govern their projects and the impact of governance files on communities.

Research consumption has been traditionally limited to the reading of academic papers—a static, dense, and formally written format. Alternatively, pre-recorded conference presentation videos, which are more dynamic, concise, and colloquial, have recently become more widely available but potentially under-utilized. In this work, we explore the design space and benefits for combining academic papers and talk videos to leverage their complementary nature to provide a rich and fluid research consumption experience. Based on formative and co-design studies, we present Papeos, a novel reading and authoring interface that allow authors to augment their papers by segmenting and localizing talk videos alongside relevant paper passages with automatically generated suggestions. With Papeos, readers can visually skim a paper through clip thumbnails, and fluidly switch between consuming dense text in the paper or visual summaries in the video. In a comparative lab study (n=16), Papeos reduced mental load, scaffolded navigation, and facilitated more comprehensive reading of papers.

When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise from multiple sources, such as ambiguity of the item being judged due to limited context, or disagreements among the participants due to different perspectives and an under-specified task. A one-size-fits-all intervention may be ineffective if it is not targeted to the right source of uncertainty. In this paper we introduce a new workflow, Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a targeted manner. By utilizing measurements that separate different sources of uncertainty during an initial round of judgment elicitation, we can then select a targeted intervention adding context or deliberation to most effectively reduce uncertainty on each item being judged. We test our approach on two tasks: rating word pair similarity and toxicity of online comments, showing that targeted interventions reduced uncertainty for the most uncertain cases. In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We also found through a simulation that our targeted approach reduced the average uncertainty scores for both sources of uncertainty as opposed to uniform approaches where reductions in average uncertainty from one source came with an increase for the other.

In recent years, industry leaders and researchers have proposed to use technical provenance standards to address visual misinformation spread through digitally altered media. By adding immutable and secure provenance information such as authorship and edit date to media metadata, social media users could potentially better assess the validity of the media they encounter. However, it is unclear how end users would respond to provenance information, or how to best design provenance indicators to be understandable to laypeople. We conducted an online experiment with 595 participants from the US and UK to investigate how provenance information altered users' accuracy perceptions and trust in visual content shared on social media. We found that provenance information often lowered trust and caused users to doubt deceptive media, particularly when it revealed that the media was composited. We additionally tested conditions where the provenance information itself was shown to be incomplete or invalid, and found that these states have a significant impact on participants' accuracy perceptions and trust in media, leading them, in some cases, to disbelieve honest media. Our findings show that provenance, although enlightening, is still not a concept well-understood by users, who confuse media credibility with the orthogonal (albeit related) concept of provenance credibility. We discuss how design choices may contribute to provenance (mis)understanding, and conclude with implications for usable provenance systems, including clearer interfaces and user education.

Online social platforms centered around content creators often allow comments on content, where creators can then moderate the comments they receive. As creators can face overwhelming numbers of comments, with some of them harassing or hateful, platforms typically provide tools such as word filters for creators to automate aspects of moderation. From needfinding interviews with 19 creators about how they use existing tools, we found that they struggled with writing good filters as well as organizing and revising their filters, due to the difficulty of determining what the filters actually catch. To address these issues, we present FilterBuddy, a system that supports creators in authoring new filters or building from pre-made ones, as well as organizing their filters and visualizing what comments are captured by them over time. We conducted an early-stage evaluation of FilterBuddy with YouTube creators, finding that participants see FilterBuddy not just as a moderation tool, but also a means to organize their comments to better understand their audiences.

When reading a scholarly paper, scientists oftentimes wish to understand how follow-on work has built on or engages with what they are reading. While a paper itself can only discuss prior work, some scientific search engines can provide a list of all subsequent citing papers; unfortunately, they are undifferentiated and disconnected from the contents of the original reference paper. In this work, we introduce a novel paper reading experience that integrates relevant information about follow-on work directly into a paper, allowing readers to learn about newer papers and see how a paper is discussed by its citing papers in the context of the reference paper. We built a tool, called CiteRead, that implements the following three contributions: 1) automated techniques for selecting important citing papers, building on results from a formative study we conducted, 2) an automated process for localizing commentary provided by citing papers to a place in the reference paper, and 3) an interactive experience that allows readers to seamlessly alternate between the reference paper and information from citing papers (e.g., citation sentences), placed in the margins. Based on a user study with 12 scientists, we found that in comparison to having just a list of citing papers and their citation sentences, the use of CiteRead while reading allows for better comprehension and retention of information about follow-on work.

Platform operators have devoted significant effort to combating misinformation on behalf of their users. Users are also stakeholders in this battle, but their efforts to combat misinformation go unsupported by the platforms. In this work, we consider three new user affordances that give social media users greater power in their fight against misinformation: (1) the ability to provide structured accuracy assessments of posts, (2) user-specified indication of trust in other users, and (3) and user configuration of social feed filters according to assessed accuracy. To understand the potential of these designs, we conducted a need-finding survey of 192 people who share and discuss news on social media, finding that many already act to limit or combat misinformation, albeit by repurposing existing platform affordances that lack customized structure for information assessment. We then conducted a field study of a prototype social media platform that implements these user affordances as structured inputs to directly impact how and whether posts are shown. The study involved 14 participants who used the platform for a week to share news while collectively assessing their accuracy. We report on users' perception and use of these affordances. We also provide design implications for platforms and researchers based on our empirical observations.

Headlines play a critical role in how users perceive articles. But many headline publishers craft headlines in ways that either attract clicks in an attempt to earn ad revenue, or misinform users or manipulate their opinions for malicious intents. Such headlines can do harm since many users simply skim and share headlines without reading the articles in full. We present an exploratory browser extension that empowers users to suggest headlines they deem better for news articles. Users can view headlines suggested by other users that they follow as they browse websites. We conducted a study of 27 users who used the extension for one week to read news and suggest headlines. We found that users saw value in the tool and used it to change headlines that they found in need of improvement. We characterize the changes that people make to headlines if enabled. We also report on a followup study we conducted with 312 participants to evaluate headlines suggested by the tool. The purpose of the study was to examine whether headlines suggested by untrained users could be preferred over original headlines by professional editors. We found that a substantial number of the suggested headlines were indeed preferred. Our work explores the designs for, and opportunities and consequences of, empowering news consumers by giving them control over the content curation process.

While research continues to investigate and improve the accuracy, fairness, and normative appropriateness of content moderation processes on large social media platforms, even the best process cannot be effective if users reject its authority as illegitimate. We present a survey experiment comparing the perceived institutional legitimacy of four popular content moderation processes. We conducted a within-subjects experiment in which we showed US Facebook users moderation decisions and randomized the description of whether those decisions were made by paid contractors, algorithms, expert panels, or juries of users. Prior work suggests that juries will have the highest perceived legitimacy due to the benefits of judicial independence and democratic representation. However, expert panels had greater perceived legitimacy than algorithms or juries. Moreover, outcome alignment -agreement with the decision - played a larger role than process in determining perceived legitimacy. These results suggest benefits to incorporating expert oversight in content moderation and underscore that any process will face legitimacy challenges derived from disagreement about outcomes.

Society is showing signs of strong ideological polarization. When pushed to seek perspectives different from their own, people often reject diverse ideas or find them unfathomable. Work has shown that framing controversial issues using the values of the audience can improve understanding of opposing views. In this paper, we present our work designing systems for addressing ideological division through educating U.S. news consumers to engage using a framework of fundamental human values known as Moral Foundations. We design and implement a series of new features that encourage users to challenge their understanding of opposing views, including annotation of moral frames in news articles, discussion of those frames via inline comments, and recommendations based on relevant moral frames. We describe two versions of features---the first covering a suite of ways to interact with moral framing in news, and the second tailored towards collaborative annotation and discussion. We conduct a field evaluation of each design iteration with 71 participants in total over a period of 6-8 days, finding evidence suggesting users learned to re-frame their discourse in moral values of the opposing side. Our work provides several design considerations for building systems to engage with moral framing.

Human ratings have become a crucial resource for training and evaluating machine learning systems. However, traditional elicitation methods for absolute and comparative rating suffer from issues with consistency and often do not distinguish between uncertainty due to disagreement between annotators and ambiguity inherent to the item being rated. In this work, we present Goldilocks, a novel crowd rating elicitation technique for collecting calibrated scalar annotations that also distinguishes inherent ambiguity from inter-annotator disagreement. We introduce two main ideas: grounding absolute rating scales with examples and using a two-step bounding process to establish a range for an item's placement. We test our designs in three domains: judging toxicity of online comments, estimating satiety of food depicted in images, and estimating age based on portraits. We show that (1) Goldilocks can improve consistency in domains where interpretation of the scale is not universal, and that (2) representing items with ranges lets us simultaneously capture different sources of uncertainty leading to better estimates of pairwise relationship distributions.

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges---1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate---on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.

Governance in online communities is an increasingly high-stakes challenge, and yet many basic features of offline governance legacies-juries, political parties, term limits, and formal debates, to name a few-are not in the feature-sets of the software most community platforms use. Drawing on the paradigm of Institutional Analysis and Development, this paper proposes a strategy for addressing this lapse by specifying basic features of a generalizable paradigm for online governance called Modular Politics. Whereas classical governance typologies tend to present a choice among wholesale ideologies, such as democracy or oligarchy, Modular Politics would enable platform operators and their users to build bottom-up governance processes from computational components that are modular and composable, highly versatile in their expressiveness, portable from one context to another, and interoperable across platforms. This kind of approach could implement pre-digital governance systems as well as accelerate innovation in uniquely digital techniques. As diverse communities share and connect their components and data, governance could occur through a ubiquitous network layer. To that end, this paper proposes the development of an open standard for networked governance.

Users have long struggled to extract and repurpose data from websites by laboriously copying or scraping content from web pages. An alternative is to write scripts that pull data through APIs. This provides a cleaner way to access data than scraping; however, APIs are effortful for programmers and nigh-impossible for non-programmers to use. In this work, we empower users to access APIs without programming. We evolve a schema for declaratively specifying how to interact with a data API. We then develop ScrAPIr: a standard query GUI that enables users to fetch data through any API for which a specification exists, and a second GUI that lets users author and share the specification for a given API. From a lab evaluation, we find that even non-programmers can access APIs using ScrAPIr, while programmers can access APIs 3.8 times faster on average using ScrAPIr than using programming.

As concerns have grown regarding harmful content spread on social media, platform mechanisms for content moderation have become increasingly significant. However, many existing platform governance structures lack formal processes for democratic participation by users of the platform. Drawing inspiration from constitutional jury trials in many legal systems, this paper proposes digital juries as a civics-oriented approach for adjudicating content moderation cases. Building on existing theoretical models of jury decision-making, we outline a 5-stage model characterizing the space of design considerations in a digital jury process. We implement two examples of jury designs involving blind-voting and deliberation. From users who participate in our jury implementations, we gather informed judgments of the democratic legitimacy of a jury process for content moderation. We find that digital juries are perceived as more procedurally just than existing common platform moderation practices, but also find disagreement over whether jury decisions should be enforced or used as recommendations.

Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses.

This joint panel between UIST and CSCW brings together leading researchers at the intersection of the conferences-systems researchers in collaborative and social computing-to engage in a discussion and retrospective. Pairs of panelists will represent each decade since the founding of the conferences, sharing a brief retrospective that surveys the most influential papers of that decade, the zeitgeist of the problems that were popular that decade and why, and what each decade's work has to say to the decades that came before and after. The panel is intended as a space to celebrate advances in the field, and reflect on the burdens and opportunities that it faces ahead.

The software behind online community platforms encodes a governance model that represents a strikingly narrow set of governance possibilities focused on moderators and administrators. When online communities desire other forms of government, such as ones that take many members? opinions into account or that distribute power in non-trivial ways, communities must resort to laborious manual effort. In this paper, we present PolicyKit, a software infrastructure that empowers online community members to concisely author a wide range of governance procedures and automatically carry out those procedures on their home platforms. We draw on political science theory to encode community governance into policies, or short imperative functions that specify a procedure for determining whether a user-initiated action can execute. Actions that can be governed by policies encompass everyday activities such as posting or moderating a message, but actions can also encompass changes to the policies themselves, enabling the evolution of governance over time. We demonstrate the expressivity of PolicyKit through implementations of governance models such as a random jury deliberation, a multi-stage caucus, a reputation system, and a promotion procedure inspired by Wikipedia's Request for Adminship (RfA) process.

Misinformation about critical issues such as climate change and vaccine safety is oftentimes amplified on online social and search platforms. The crowdsourcing of content credibility assessment by laypeople has been proposed as one strategy to combat misinformation by attempting to replicate the assessments of experts at scale. In this work, we investigate news credibility assessments by crowds versus experts to understand when and how ratings between them differ. We gather a dataset of over 4,000 credibility assessments taken from 2 crowd groups---journalism students and Upwork workers---as well as 2 expert groups---journalists and scientists---on a varied set of 50 news articles related to climate science, a topic with widespread disconnect between public opinion and expert consensus. Examining the ratings, we find differences in performance due to the makeup of the crowd, such as rater demographics and political leaning, as well as the scope of the tasks that the crowd is assigned to rate, such as the genre of the article and partisanship of the publication. Finally, we find differences between expert assessments due to differing expert criteria that journalism versus science experts use---differences that may contribute to crowd discrepancies, but that also suggest a way to reduce the gap by designing crowd tasks tailored to specific expert criteria. From these findings, we outline future research directions to better design crowd processes that are tailored to specific crowds and types of content.

Local governments still depend on traditional town halls for community consultation, despite problems such as a lack of inclusive participation for attendees and difficulty for civic organizers to capture attendees' feedback in reports. Building on a formative study with 66 town hall attendees and 20 organizers, we designed and developed CommunityClick, a communitysourcing system that captures attendees' feedback in an inclusive manner and enables organizers to author more comprehensive reports. During the meeting, in addition to recording meeting audio to capture vocal attendees' feedback, we modify iClickers to give voice to reticent attendees by allowing them to provide real-time feedback beyond a binary signal. This information then automatically feeds into a meeting transcript augmented with attendees' feedback and organizers' tags. The augmented transcript along with a feedback-weighted summary of the transcript generated from text analysis methods is incorporated into an interactive authoring tool for organizers to write reports. From a field experiment at a town hall meeting, we demonstrate how CommunityClick can improve inclusivity by providing multiple avenues for attendees to share opinions. Additionally, interviews with eight expert organizers demonstrate CommunityClick's utility in creating more comprehensive and accurate reports to inform critical civic decision-making. We discuss the possibility of integrating CommunityClick with town hall meetings in the future as well as expanding to other domains.

In many instances of online collaboration, ideation and deliberation about what to write happen separately from the synthesis of the deliberation into a cohesive document. However, this may result in a final document that has little connection to the discussion that came before. In this work, we present interleaved discussion and summarization, a process where discussion and summarization are woven together in a single space, and collaborators can switch back and forth between discussing ideas and summarizing discussion until it results in a final document that incorporates and references all discussion points. We implement this process into a tool called Wikum+ that allows groups working together on a project to create living summaries-artifacts that can grow as new collaborators, ideas, and feedback arise and shrink as collaborators come to consensus. We conducted studies where groups of six people each collaboratively wrote a proposal using Wikum+ and a proposal using a messaging platform along with Google Docs. We found that Wikum+'s integration of discussion and summarization helped users be more organized, allowing for light-weight coordination and iterative improvements throughout the collaboration process. A second study demonstrated that in larger groups, Wikum+ is more inclusive of all participants and more comprehensive in the final document compared to traditional tools.

Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.

Email management consumes significant effort from senders and recipients. Some of this work might be automatable. We performed a mixed-methods need-finding study to learn: (i) what sort of automatic email handling users want, and (ii) what kinds of information and computation are needed to support that automation. Our investigation included a design workshop to identify categories of needs, a survey to better understand those categories, and a classification of existing email automation software to determine which needs have been addressed. Our results highlight the need for: a richer data model for rules, more ways to manage attention, leveraging internal and external email context, complex processing such as response aggregation, and affordances for senders. To further investigate our findings, we developed a platform for authoring small scripts over a user's inbox. Of the automations found in our studies, half are impossible in popular email clients, motivating new design directions.

Communication platforms have struggled to provide effective tools for people facing harassment online. We conducted interviews with 18 recipients of online harassment to understand their strategies for coping, finding that they often resorted to asking friends for help. Inspired by these findings, we explore the feasibility of friendsourced moderation as a technique for combating online harassment. We present Squadbox, a tool to help recipients of email harassment coordinate a "squad" of friend moderators to shield and support them during attacks. Friend moderators intercept email from strangers and can reject, organize, and redirect emails, as well as collaborate on filters. Squadbox is designed to let its users implement highly customized workflows, as we found in interviews that harassment and preferences for mitigating it vary widely. We evaluated Squadbox on five pairs of friends in a field study, finding that participants could comfortably navigate around privacy and personalization concerns.

Resolving disputes in a timely manner is crucial for any online production group. We present an analysis of Requests for Comments (RfCs), one of the main vehicles on Wikipedia for formally resolving a policy or content dispute. We collected an exhaustive dataset of 7,316 RfCs on English Wikipedia over the course of 7 years and conducted a qualitative and quantitative analysis into what issues affect the RfC process. Our analysis was informed by 10 interviews with frequent RfC closers. We found that a major issue affecting the RfC process is the prevalence of RfCs that could have benefited from formal closure but that linger indefinitely without one, with factors including participants' interest and expertise impacting the likelihood of resolution. From these findings, we developed a model that predicts whether an RfC will go stale with 75.3% accuracy, a level that is approached as early as one week after dispute initiation.

While group chat is becoming increasingly popular for team collaboration, these systems generate long streams of unstructured back-and-forth discussion that are difficult to comprehend. In this work, we investigate ways to enrich the representation of chat conversations, using techniques such as tagging and summarization, to enable users to better make sense of chat. Through needfinding interviews with 15 active group chat users, who were shown mock-up alternative chat designs, we found the importance of structured representations, including signals such as discourse acts. We then developed Tilda, a prototype system that enables people to collaboratively enrich their chat conversation while conversing. From lab evaluations, we examined the ease of marking up chat using Tilda as well as the effectiveness of Tilda-enabled summaries for getting an overview. From a field deployment, we found that teams actively engaged with Tilda both for marking up their chat as well as catching up on chat.

When searching on the web or in an app, results are often returned as lists of hundreds to thousands of items, making it difficult for users to understand or navigate the space of results. Research has demonstrated that using clustering to partition search results into coherent, topical clusters can aid in both exploration and discovery. Yet clusters generated by an algorithm for this purpose are often of poor quality and do not satisfy users. To achieve acceptable clustered search results, experts must manually evaluate and refine the clustered results for each search query, a process that does not scale to large numbers of search queries. In this article, we investigate using crowd-based human evaluation to inspect, evaluate, and improve clusters to create high-quality clustered search results at scale. We introduce a workflow that begins by using a collection of well-known clustering algorithms to produce a set of clustered search results for a given query. Then, we use crowd workers to holistically assess the quality of each clustered search result to find the best one. Finally, the workflow has the crowd spot and fix problems in the best result to produce a final output. We evaluate this workflow on 120 top search queries from the Google Play Store, some of whom have clustered search results as a result of evaluations and refinements by experts. Our evaluations demonstrate that the workflow is effective at reproducing the evaluation of expert judges and also improves clusters in a way that agrees with experts and crowds alike.

Large-scale discussions between many participants abound on the internet today, on topics ranging from political arguments to group coordination. But as these discussions grow to tens of thousands of posts, they become ever more difficult for a reader to digest. In this article, we describe a workflow called recursive summarization, implemented in our Wikum prototype, that enables a large population of readers or editors to work in small doses to refine out the main points of the discussion. More than just a single summary, our workflow produces a summary tree that enables a reader to explore distinct subtopics at multiple levels of detail based on their interests. We describe lab evaluations showing that (i) Wikum can be used more effectively than a control to quickly construct a summary tree and (ii) the summary tree is more effective than the original discussion in helping readers identify and explore the main topics.

Determining affective states such as confusion from students' participation in online discussion forums can be useful for instructors of a large classroom. However, manual annotation of forum posts by instructors or paid crowd workers is both time-consuming and expensive. In this work, we harness affordances prevalent in social media to allow students to self-annotate their discussion posts with a set of hashtags and emojis, a process that is fast and cheap. For students, self-annotation with hashtags and emojis provides another channel for self-expression, as well as a way to signal to instructors and other students on the lookout for certain types of messages. This method also provides an easy way to acquire a labeled dataset of affective states, allowing us distinguish between more nuanced emotions such as confusion and curiosity. From a dataset of over 25,000 discussion posts from two courses containing self-annotated posts by students, we demonstrate how we can identify linguistic differences between posts expressing confusion versus curiosity, achieving 83% accuracy at distinguishing between the two affective states.

In the past few years an unprecedented wave of anti-abortion policies were introduced and enacted in state governments in the U.S., affecting millions of constituents. We study this rapid spread of policy change as a function of the underlying ideology of constituents. We examine over 200,000 public messages posted on Twitter surrounding abortion in the year 2013, a year that saw 82 new anti-abortion policies enacted. From these posts, we characterize people's expressions of opinion on abortion and show how these expressions align with policy change on these issues. We detail a number of ideological differences between constituents in states enacting anti versus pro-abortion policies, such as a tension between the moral values of purity versus fairness, and a differing emphasis on the fetus versus the pregnant woman. We also find significant differences in how males versus females discuss the issue of abortion, including greater emphasis on health and religion by males. Using these measures to characterize states, we can construct models to explain the spread of abortion policy from state to state and project which types of abortion policies a state will introduce. Models defining state similarity using our Twitter-based measures improved policy projection accuracy by 7.32% and 12.02% on average over geographic and poll-based ideological similarity, respectively. Additionally, models constructed from the expressions of male-only constituents perform better than models from the expressions of female-only constituents, suggesting that the ideology of men is more aligned with the recent spread of anti-abortion legislation than that of women.

While the web contains many social websites, people are generally left in the dark about the activities of other people traversing the web as a whole. In this paper, we explore the potential benefits and privacy considerations around generating a real-time, publicly accessible stream of web activity where users can publish chosen parts of their web browsing data. Taking inspiration from social media systems, we describe individual benefits that can be unlocked by such sharing and that may incentivize users to publish aspects of their browsing. We ask whether and how these benefits outweigh potential costs in lost privacy. We conduct our study of public web activity sharing through scenario-based interviews and a field deployment of a tool for web activity sharing.

Many people can author static web pages with HTML and CSS but find it hard or impossible to program persistent, interactive web applications. We show that for a broad class of CRUD (Create, Read, Update, Delete) applications, this gap can be bridged. Mavo extends the declarative syntax of HTML to describe Web applications that manage, store and transform data. Using Mavo, authors with basic HTML knowledge define complex data schemas implicitly as they design their HTML layout. They need only add a few attributes and expressions to their HTML elements to transform their static design into a persistent, data-driven web application whose data can be edited by direct manipulation of the content in the browser. We evaluated Mavo with 20 users who marked up static designs---some provided by us, some their own creation---to transform them into fully functional web applications. Even users with no programming experience were able to quickly craft Mavo applications.

Mailing lists have existed since the early days of email and are still widely used today, even as more sophisticated online forums and social media websites proliferate. The simplicity of mailing lists can be seen as a reason for their endurance, a source of dissatisfaction, and an opportunity for improvement. Using a mixed-method approach, we studied two community mailing lists in depth with interviews and surveys, and surveyed a broader spectrum of 28 lists. We report how members of the different communities use their lists and their goals and desires for them. We explore why members prefer mailing lists to other group communication tools. But we also identify several tensions around mailing list usage that appear to contribute to dissatisfaction with them. We conclude with design implications, discussing ways to alleviate these tensions while preserving mailing lists' appeal.

Social media has emerged as a prominent platform where people can express their feelings about social and political issues of our time. We study the many voices discussing an issue within a constituency and how they reflect ideology and may signal the outcome of important policy decisions. Focusing on the issue of same-sex marriage legalization, we examine almost 2 million public Twitter posts related to same-sex marriage in the U.S. states over the course of 4 years starting from 2011. Among other findings, we find evidence of moral culture wars between ideologies and show that constituencies that express higher levels of emotion and have fewer actively engaged participants often precede legalization efforts that fail. From our measures, we build statistical models to predict the outcome of potential policy changes, with our best model achieving 87% accuracy. We also achieve accuracies of 70%, comparable to public opinion surveys, many months before a policy decision. We discuss how these analyses can augment traditional political science techniques as well as assist activists and policy analysts in understanding discussions on important issues at a population scale.

Creating and publishing read-write-compute web applications requires programming skills beyond what most end users possess. But many end users know how to make spreadsheets that act as simple information management applications, some even with computation. We present a system for creating basic web applications using such spreadsheets in place of a server and using HTML to describe the client UI. Authors connect the two by placing spreadsheet references inside HTML attributes. Data computation is provided by spreadsheet formulas. The result is a reactive read-write-compute web page without a single line of Javascript code. Nearly all of the fifteen HTML novices we studied were able to connect HTML to spreadsheets using our method with minimal instruction. We draw conclusions from their experience and discuss future extensions to this programming model.

Long documents are abundant on the web today, and are accessed in increasing numbers from touchscreen devices such as mobile phones and tablets. Navigating long documents with small screens can be challenging both physically and cognitively because they compel the user to scroll a great deal and to mentally filter for important content. To support navigation of long documents on touchscreen devices, we introduce content-aware kinetic scrolling, a novel scrolling technique that dynamically applies pseudo-haptic feedback in the form of friction around points of high interest within the page. This allows users to quickly find interesting content while exploring without further cluttering the limited visual space. To model degrees of interest (DOI) for a variety of existing web pages, we introduce social wear, a method for capturing DOI based on social signals that indicate collective user interest. Our preliminary evaluation shows that users pay attention to items with kinetic scrolling feedback during search, recognition, and skimming tasks.

One of the most significant challenges for many online communities is increasing members' contributions over time. Prior studies on peer feedback in online communities have suggested its impact on contribution, but have been limited by their correlational nature. In this paper, we conducted a field experiment on Wikipedia to test the effects of different feedback types (positive feedback, negative feedback, directive feedback, and social feedback) on members' contribution. Our results characterize the effects of different feedback types, and suggest trade-offs in the effects of feedback between the focal task and general motivation, as well as differences in how newcomers and experienced editors respond to peer feedback. This research provides insights into the mechanisms underlying peer feedback in online communities and practical guidance to design more effective peer feedback systems.