Search

Content moderation systems for social media have had numerous issues of bias, in terms of race, gender, and ability among many others. One proposal for addressing such issues in automated decision making is by designing for contestability, whereby users can shape and influence how decisions are made. In this study, we conduct a series of participatory design workshops with participants from communities that have experienced problems with social media content moderation in the past. Together with participants, we explore the idea of designing for contestability in content moderation and find that users' designs suggest three fruitful, practical avenues: adding representation, improving communication, and designing with compassion. We conclude with design recommendations drawn from participants' proposals, and reflect on the challenges that remain.

Data visualization is the primary means by which data analysts explore patterns, trends, and insights in their data. Unfortunately, existing visual analytics tools offer limited expressiveness and scalability when it comes to searching for visualizations over large datasets, making visual data exploration labor-intensive and timeconsuming. We first discuss our prior work on Zenvisage that helps accelerate exploratory data analysis via an interactive interface and an expressive visualization query language, but offers limited flexibility when the pattern of interest is under-specified and approximate. Motivated from our findings from Zenvisage, we develop ShapeSearch, an efficient and flexible pattern-searching tool that enables the search for desired patterns via multiple mechanisms: sketch, natural-language, and visual regular expressions. ShapeSearch leverages a novel shape querying algebra that can express a large class of shape queries and supports query-aware and perceptually-aware optimizations to execute shape queries within interactive response times. To further improve the usability and performance of both Zenvisage and ShapeSearch, we discuss a number of open research problems.

Surveys are a common instrument to gauge self-reported opinions from the crowd for scholars in the CSCW community, the social sciences, and many other research areas. Researchers often use surveys to prioritize a subset of given options when there are resource constraints. Over the past century, researchers have developed a wide range of surveying techniques, including one of the most popular instruments, the Likert ordinal scale, to elicit individual preferences. However, the challenge to elicit accurate and rich self-reported responses with surveys in a resource-constrained context still persists today. In this study, we examine Quadratic Voting (QV), a voting mechanism powered by the affordances of a modern computer and straddles ratings and rankings approaches, as an alternative online survey technique. We argue that QV could elicit more accurate self-reported responses compared to the Likert scale when the goal is to understand relative preferences under resource constraints. We conducted two randomized controlled experiments on Amazon Mechanical Turk, one in the context of public opinion polling and the other in a human-computer interaction user study. Based on our Bayesian analysis results, a QV survey with a sufficient amount of voice credits, aligned significantly closer to participants' incentive-compatible behaviors than a Likert scale survey, with a medium to high effect size. In addition, we extended QV's application scenario from typical public policy and education research to a problem setting familiar to the CSCW community: a prototypical HCI user study. Our experiment results, QV survey design, and QV interface serve as a stepping stone for CSCW researchers to further explore this surveying methodology in their studies and encourage decision-makers from other communities to consider QV as a promising alternative.

Spreadsheet systems are by far the most popular platform for data exploration on the planet, supporting millions of rows of data. However, exploring spreadsheets that are this large via operations such as scrolling or issuing formulae can be overwhelming and error-prone. Users easily lose context and suffer from cognitive and mechanical burdens while issuing formulae on data spanning multiple screens. To address these challenges, we introduce dynamic hierarchical overviews that are embedded alongside spreadsheets. Users can employ this overview to explore the data at various granularities, zooming in and out of the spreadsheet. They can issue formulae over data subsets without cumbersome scrolling or range selection, enabling users to gain a high or low-level perspective of the spreadsheet. An implementation of our dynamic hierarchical overview, NOAH, integrated within DataSpread, preserves spreadsheet semantics and look and feel, while introducing such enhancements. Our user studies demonstrate that NOAH makes it more intuitive, easier, and faster to navigate spreadsheet data compared to traditional spreadsheets like Microsoft Excel and spreadsheet plug-ins like Pivot Table, for a variety of exploration tasks; participants made fewer mistakes in NOAH while being faster in completing the tasks.

Interest has grown in designing algorithmic decision making systems for contestability. In this work, we study how users experience contesting unfavorable social media content moderation decisions. A large-scale online experiment tests whether different forms of appeals can improve users' experiences of automated decision making. We study the impact on users' perceptions of the Fairness, Accountability, and Trustworthiness of algorithmic decisions, as well as their feelings of Control (FACT). Surprisingly, we find that none of the appeal designs improve FACT perceptions compared to a no appeal baseline. We qualitatively analyze how users write appeals, and find that they contest the decision itself, but also more fundamental issues like the goal of moderating content, the idea of automation, and the inconsistency of the system as a whole. We conclude with suggestions for -- as well as a discussion of the challenges of -- designing for contestability.

This paper examines the use of the abstract comic form for persuading online charitable donations. Persuading individuals to contribute to charitable causes online is hard and responses to the appeals are typically low; charitable donations share the structure of public goods dilemmas where the rewards are distant and non-exclusive. In this paper, we examine if comics in abstract form are more persuasive than in the plain text form. Drawing on a rich literature on comics, we synthesized a three-panel abstract comic to create our appeal. We conducted a between-subject study with 307 participants from Amazon Mechanical Turk on the use of abstract comic form to appeal for charitable donations. As part of our experimental procedure, we sought to persuade individuals to contribute to a real charity focused on Autism research with monetary costs. We compared the average amount of donation to the charity under three conditions: the plain text message, an abstract comic that includes the plain text, and an abstract comic that additionally includes the social proof. We use Bayesian modeling to analyze the results, motivated by model transparency and its use in small-sized studies. Our experiments reveal that the message in abstract comic form elicited significantly more donations than text form (medium to large effect size=0.59). Incorporating social proof in the abstract comic message did not show a significant effect. Our studies have design implications: non-profits and governmental agencies interested in alleviating public goods dilemmas that share a similar structure to our experiment (single-shot task, distant, non-exclusive reward) ought to consider including messages in the abstract comic form as part of their online fund-raising campaign.

With a plethora of off-the-shelf smart home devices available commercially, people are increasingly taking a do-it-yourself approach to configuring their smart homes. While this allows for customization, users responsible for smart home configuration often end up with more control over the devices than other household members. This separates those who introduce new functionality to the smart home (pilot users) from those who do not (passenger users). To investigate the prevalence and impact of pilot-passenger user relationships, we conducted a Mechanical Turk survey and a series of one-hour interviews. Our results suggest that pilot-passenger relationships are common in multi-user households and shape how people form habits around devices. We find from interview data that smart homes reflect the values of their pilot users, making it harder for passenger users to incorporate their devices into daily life. We conclude the paper with design recommendations to improve passenger and pilot user experience.

Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our paper explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, Frontier, that implements these categories. Using Frontier, we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories., 10 pages. This work has been submitted to IEEE TVCG. Copyright may be transferred without notice, after which this version may no longer be accessible

Spreadsheet systems are used for storing and analyzing data across domains by programmers and non-programmers alike.While spreadsheet systems have continued to support increasingly large datasets, they are prone to hanging and freezing while performing computations even on much smaller ones. We present a benchmarking study that evaluates and compares the performance of three popular systems, Microsoft Excel, LibreOffice Calc, and Google Sheets, on a range of canonical spreadsheet computation operations. We find that spreadsheet systems lack interactivity for several operations, on datasets well below their advertised scalability limits. We further evaluate whether spreadsheet systems adopt database optimization techniques such as indexing, intelligent data layout, and incremental and shared computation,to efficiently execute computation operations. We outline several ways future spreadsheet systems can be redesigned to offer interactive response times on large datasets.

Among many young people, the creation of a finsta-a portmanteau of "fake" and "Instagram" which describes secondary Instagram accounts-provides an outlet to share emotional, low-quality, or indecorous content with their close friends. To study why people create and maintain finstas, we conducted a qualitative study through interviews with finsta users and content analysis of video bloggers exposing their finsta on YouTube. We found that one way that young people deal with mounting social pressures is by reconfiguring online platforms and changing their purposes, norms, expectations, and currencies. Carving out smaller spaces accessible only to close friends allows users the opportunity for a more unguarded, vulnerable, and unserious performance. Drawing on feminist theory, we term this process intimate reconfiguration. Through this reconfiguration finsta users repurpose an existing and widely-used social platform to create opportunities for more meaningful and reciprocal forms of social support.

Team formation tools assume instructors should configure the criteria for creating teams, precluding students from participating in a process affecting their learning experience. We propose LIFT, a novel learner-centered workflow where students propose, vote for, and weigh the criteria used as inputs to the team formation algorithm. We conducted an experiment (N=289) comparing LIFT to the usual instructor-led process, and interviewed participants to evaluate their perceptions of LIFT and its outcomes. Learners proposed novel criteria not included in existing algorithmic tools, such as organizational style. They avoided criteria like gender and GPA that instructors frequently select, and preferred those promoting efficient collaboration. LIFT led to team outcomes comparable to those achieved by the instructor-led approach, and teams valued having control of the team formation process. We provide instructors and designers with a workflow and evidence supporting giving learners control of the algorithmic process used for grouping them into teams.

Data visualization is an effective mechanism for identifying trends, insights, and anomalies in data. On large datasets, however, generating visualizations can take a long time, delaying the extraction of insights, hampering decision making, and reducing exploration time. One solution is to use online sampling-based schemes to generate visualizations faster while improving the displayed estimates incrementally, eventually converging to the exact visualization computed on the entire data. However, the intermediate visualizations are approximate, and often fluctuate drastically, leading to potentially incorrect decisions. We propose sampling-based incremental visualization algorithms that reveal the "salient" features of the visualization quickly---with a 46× speedup relative to baselines---while minimizing error, thus enabling rapid and error-free decision making. We demonstrate that these algorithms are optimal in terms of sample complexity, in that given the level of interactivity, they generate approximations that take as few samples as possible. We have developed the algorithms in the context of an incremental visualization tool, titled I nc V isage , for trendline and heatmap visualizations. We evaluate the usability of I nc V isage via user studies and demonstrate that users are able to make effective decisions with incrementally improving visualizations, especially compared to vanilla online-sampling based schemes.

Even though electrodermal activity has been widely used in the study of psychological states and processes for over 130 years, the use of such technology in situ, within the context of daily activities, remains a major challenge. Recent technological advancements have led to the development of wearable biosensors that noninvasively measure electrical conductance across the skin. These biosensors represent a new approach for skin conductance assessment, as a proxy for emotional arousal, in children with neurodevelopmental communication impairments who are often described as having difficulties with emotional regulation, expressing thoughts and feelings, and present a higher prevalence of challenging behaviors. Here we provide an overview of skin conductance and explore the benefits of recent technological advancements for applied research and clinical practice. We draw on user experience from two experimental interventions involving eight children with neurodevelopmental impairments. In both cases investigators monitored phasic and tonic EDA measures in situ using wearable biosensors. We share the behavioral and technical challenges experienced across these two experimental contexts, and propose associated considerations for future use. Specifically, sensor functioning, synchronization, and data preprocessing/analysis difficulties, as well as behavioral findings related to developmental differences, sensor tolerance over time, and sensor placement are discussed.

Algorithms exert great power in curating online information, yet are often opaque in their operation, and even existence. Since opaque algorithms sometimes make biased or deceptive decisions, many have called for increased transparency. However, little is known about how users perceive and interact with potentially biased and deceptive opaque algorithms. What factors are associated with these perceptions, and how does adding transparency into algorithmic systems change user attitudes? To address these questions, we conducted two studies: 1) an analysis of 242 users' online discussions about the Yelp review filtering algorithm and 2) an interview study with 15 Yelp users disclosing the algorithm's existence via a tool. We found that users question or defend this algorithm and its opacity depending on their engagement with and personal gain from the algorithm. We also found adding transparency into the algorithm changed users' attitudes towards the algorithm: users reported their intention to either write for the algorithm in future reviews or leave the platform.

Spreadsheet tools are ubiquitous for interactive adhoc data management and analysis. With increasing dataset sizes, spreadsheet tools fall short—they freeze during heavy computation within the sheet (interactivity); they are hard to navigate when datasets go beyond a certain size (navigability); they only support cell-at-a-time computation, severely limiting analysis capabilities (expressiveness). We have been developing DATASPREAD to holistically unify databases and spreadsheets to leverage the benefits of both, with a spreadsheet-like front-end and a database-like backend. We demonstrate three key features of DATASPREAD to address the aforementioned spreadsheet scalability challenges in interactivity, navigability, and expressiveness1. Our demonstration will let attendees perform typical analysis tasks on Microsoft Excel and DATASPREAD side-by-side, providing a clear understanding of the improvements offered by DATASPREAD over traditional spreadsheet tools.

Identifying trendline visualizations with desired patterns is a common and fundamental data exploration task. Existing visual analytics tools offer limited flexibility and expressiveness for such tasks, especially when the pattern of interest is under-specified and approximate, and do not scale well when the pattern searching needs are ad-hoc, as is often the case. We propose ShapeSearch, an efficient and flexible pattern-searching tool, that enables the search for desired patterns via multiple mechanisms: sketch, natural-language, and visual regular expressions. We develop a novel shape querying algebra, with a minimal set of primitives and operators that can express a large number of ShapeSearch queries, and design a natural-language and regex-based parser to automatically parse and translate user queries to the algebra representation. To execute these queries within interactive response times, ShapeSearch uses a fast shape algebra-based execution engine with query-aware optimizations, and perceptually-aware scoring methodologies. We present a thorough evaluation of the system, including a general-purpose user study, a case study involving genomic data analysis, as well as performance experiments, comparing against state-of-the-art time series shape matching approaches---that together demonstrate the usability and scalability of ShapeSearch.

Slanted framing in news article titles induce bias and influence recall. While recent studies found that viewers focus extensively on titles when reading visualizations, the impact of titles in visualization remains underexplored. We study frames in visualization titles, and how the slanted framing of titles and the viewer's pre-existing attitude impact recall, perception of bias, and change of attitude. When asked to compose visualization titles, people used five existing news frames, an open-ended frame, and a statistics frame. We found that the slant of the title influenced the perceived main message of a visualization, with viewers deriving opposing messages from the same visualization. The results did not show any significant effect on attitude change. We highlight the danger of subtle statistics frames and viewers' unwarranted conviction of the neutrality of visualizations. Finally, we present a design implication for the generation of visualization titles and one for the viewing of titles.

Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.

People desire to present themselves favorably to others. However, medical crowdfunding beneficiaries are often expected to present their dire medical conditions and financial straits to solicit financial support. To investigate how beneficiaries convey their situation on medical crowdfunding pages and how contributors perceive the presented information, we interviewed both medical crowdfunding beneficiaries and contributors. While beneficiaries emphasized the serious of their medical situations to signal their deservedness of support, contributor participants gave less attention to that content. Rather, they focused on their impression of the beneficiary's character formed by various features of contributions such as the contributor's names, messages, and shared pictures. These contribution features further signaled common connections between the beneficiary and contributors and each contributor's unique involvement in the beneficiary's medical journey. However, the contribution amount resulted in judgement about other contributors. We suggest design opportunities and challenges that apply these results to the design of medical crowdfunding interfaces.

Algorithmic prioritization is a growing focus for social media users. Control settings are one way for users to adjust the prioritization of their news feeds, but they prioritize feed content in a way that can be difficult to judge objectively. In this work, we study how users engage with difficult-to-validate controls. Via two paired studies using an experimental system -- one interview and one online study -- we found that control settings functioned as placebos. Viewers felt more satisfied with their feed when controls were present, whether they worked or not. We also examine how people engage in sensemaking around control settings, finding that users often take responsibility for violated expectations -- for both real and randomly functioning controls. Finally, we studied how users controlled their social media feeds in the wild. The use of existing social media controls had little impact on user's satisfaction with the feed; instead, users often turned to improvised solutions, like scrolling quickly, to see what they want.

In traditional usability studies, researchers talk to users of tools to understand their needs and challenges. Insights gained via such interviews offer context, detail, and background. Due to costs in time and money, we are beginning to see a new form of tool interrogation that prioritizes scale, cost, and breadth by utilizing existing data from online forums. In this case study, we set out to apply this method of using online forum data to a specific issue---challenges that users face with Excel spreadsheets. Spreadsheets are a versatile and powerful processing tool if used properly. However, with versatility and power come errors, from both users and the software, which make using spreadsheets less effective. By scraping posts from the website Reddit, we collected a dataset of questions and complaints about Excel. Specifically, we explored and characterized the issues users were facing with spreadsheet software in general, and in particular, as resulting from a large amount of data in their spreadsheets. We discuss the implications of our findings on the design of next-generation spreadsheet software.

Understanding the factors that persuade backers to donate to research projects has become increasingly important with the rising popularity of scientific crowdfunding. Although there are many similarities between enterprise and scientific crowdfunding, some factors differentiate these two forms of crowdfunding. One such factor is the use of endorsements. The endorsement helps backers gain trust based on expert opinions about the competency of the researchers and the usefulness of the projects. We analyzed 810 endorsements from scientific campaigns posted on Experiment.com and derived a taxonomy of topics discussed in the endorsements. A regression analysis revealed that when endorsers explained the skills of the campaign owners, the probability of success of the campaign improved; on the contrary, when endorsers reiterated the goal of the project, the campaign was less likely to succeed. We conclude with design implications formulated from our findings to better support scientific crowdfunding.

Instructors are increasingly using algorithmic tools for team formation, yet little is known about how these tools are applied or how students and instructors perceive their use. We studied a representative team formation tool (CATME) in eight project-based courses. An instructor uses the tool to form teams by surveying students' working styles, skills, and demographics; then configuring these criteria as input into an algorithm that assigns teams. We surveyed students (N=277) in the courses to gauge their perceptions of the strengths and weaknesses of the tool and ideas for improving it. We also interviewed instructors (N=13) different from those who taught the eight courses to learn about their criteria selections and perceptions of the tool. Students valued the rational basis for forming teams but desired a stronger voice in criteria selection and explanations as to why they were assigned to a particular team. Instructors appreciated the efficiency of team formation but wanted to view exemplars of criteria used in similar courses. This work contributes recommendations for deploying team formation tools in educational settings and for better satisfying the goals of all stakeholders.

Medical crowdfunding helps patients receive financial support from their distributed social networks online. However, little is known about who the patient's supporters are, what support they provide, and why. To address this, we interviewed fifteen people involved in medical crowdfunding, including both beneficiaries and supporters. We found that support networks were larger than beneficiaries expected, with strangers offering support. Supporters offered not only monetary but also volunteering contributions including campaign creation, promotion, and external support. However, the emphasis medical crowdfunding interfaces place on monetary contributions led to social issues. Beneficiaries' close friends felt pressured to donate money they could not afford to give. And beneficiaries promoting the campaign worried they would be judged for requesting money. To mitigate these concerns, we suggest making the variety of volunteering contributions more visible and discuss the design challenges of including such signals in existing systems.

Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the ranking system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter. We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems' interfaces., Comment: In Proceedings of ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW), Portland, USA, February 2017

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries., Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation methods

Traditional medical fundraising charities have been relying on third-party watchdogs and carefully crafting their reputation over time to signal their credibility to potential donors. As medical fundraising campaigns migrate to online platforms in the form of crowdfunding, potential donors can no longer rely on the organization's traditional methods for achieving credibility. Individual fundraisers must establish credibility on their own. Potential donors, therefore, seek new factors to assess the credibility of crowdfunding campaigns. In this paper, we investigate current practices in assessing the credibility of online medical crowdfunding campaigns. We report results from a mixed-methods study that analyzed data from social media and semi-structured interviews. We discovered eleven factors associated with the perceived credibility of medical crowdfunding. Of these, three communicative/emotional factors were unique to medical crowdfunding. We also found a distinctive validation practice, the collective endorsement. Close-connections' online presence and external online communities come together to form this collective endorsement in online medical fundraising campaigns. We conclude by describing how fundraisers can leverage collective endorsements to improve their campaigns' perceived credibility.

Many online platforms use curation algorithms that are opaque to the user. Recent work suggests that discovering a filtering algorithm's existence in a curated feed influences user experience, but it remains unclear how users reason about the operation of these algorithms. In this qualitative laboratory study, researchers interviewed a diverse, non-probability sample of 40 Facebook users before, during, and after being presented alternative displays of Facebook's News Feed curation algorithm's output. Interviews revealed 10 "folk theories' of automated curation, some quite unexpected. Users who were given a probe into the algorithm's operation via an interface that incorporated "seams,' visible hints disclosing aspects of automation operations, could quickly develop theories. Users made plans that depended on their theories. We conclude that foregrounding these automated processes may increase interface design complexity, but it may also add usability benefits.

Data visualization is by far the most commonly used mechanism to explore data, especially by novice data analysts and data scientists. And yet, current visual analytics tools are rather limited in their ability to guide data scientists to interesting or desired visualizations: the process of visual data exploration remains cumbersome and time-consuming. We propose zenvisage, a platform for effortlessly visualizing interesting patterns, trends, or insights from large datasets. We describe zenvisage's general purpose visual query language, ZQL ("zee-quel") for specifying the desired visual trend, pattern, or insight - ZQL draws from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra, and demonstrate that ZQL is at least as expressive as that algebra. While analysts are free to use ZQL directly, we also expose ZQL via a visual specification interface that we describe in this paper. We then describe our architecture and optimizations, preliminary experiments in supporting and optimizing for ZQL queries in our initial zenvisage prototype, and a user study to evaluate whether data scientists are able to effectively use zenvisage for real applications., Comment: Tech Report

A social mirror is a specific type of visualization for group interaction. Three examples of social mirrors emphasize different motivations for visualizing vocal conversation: the power of visualization to influence conversation in real time, the addition of anonymous input into group visualization, and idea formation over time. Social visualizations are visualizations about people, for people. A social mirror is a type of social visualization with three particular qualities. First, it's a third-person visualization in which people can see information about themselves within information about other people. In a sense, we can perceive "us and them." Every participant sees the same visualization. Second, subtle perceivable changes of any participant appear in the visualization as they occur in near real time. This can include capturing and visualizing subtle social behaviors such as laughing and coughing. Because of this near real-time feature, people can quickly alter their behavior-and hence the visualization-so that others perceive them as they'd like to be perceived in this public mirror. Third, social mirrors allow for exploring group patterns and behavior through real-time experimentation, replay, annotation, and reconfiguration.

HCI studies assessing nonverbal individuals (especially those who do not communicate through traditional linguistic means: spoken, written, or sign) are a daunting undertaking. Without the use of directed tasks, interviews, questionnaires, or question-answer sessions, researchers must rely fully upon observation of behavior, and the categorization and quantification of the participant’s actions. This problem is compounded further by the lack of metrics to quantify the behavior of nonverbal subjects in computer-based intervention contexts. We present a set of dependent variables called A3 (pronounced A-Cubed) or Annotation for ASD Analysis, to assess the behavior of this demographic of users, specifically focusing on engagement and vocalization. This paper demonstrates how theory from multiple disciplines can be brought together to create a set of dependent variables, as well as demonstration of these variables, in an experimental context. Through an examination of the existing literature, and a detailed analysis of the current state of computer vision and speech detection, we present how computer automation may be integrated with the A3 guidelines to reduce coding time and potentially increase accuracy. We conclude by presenting how and where these variables can be used in multiple research areas and with varied target populations.

In this paper we argue that social visualization can motivate contributors to social production projects, such as Wikipedia and open source development. As evidence, we present CodeSaw, a social visualization of open source software development that we studied with real open source communities. CodeSaw mines open source archives to visualize group dynamics that currently lie buried in textual databases. Furthermore, CodeSaw becomes an active social space itself by supporting comments directly inside the visualization. To demonstrate CodeSaw, we apply it to a popular open source project, showing how the visualization reveals group dynamics and individual roles. The paper concludes by presenting evidence that CodeSaw, and social visualization more generally, can motivate contributors to social production projects if the visualization leaves the laboratory and makes it to the community visualized.

Answering questions with data is a difficult and time-consuming process. Visual dashboards and templates make it easy to get started, but asking more sophisticated questions often requires learning a tool designed for expert analysts. Natural language interaction allows users to ask questions directly in complex programs without having to learn how to use an interface. However, natural language is often ambiguous. In this work we propose a mixed-initiative approach to managing ambiguity in natural language interfaces for data visualization. We model ambiguity throughout the process of turning a natural language query into a visualization and use algorithmic disambiguation coupled with interactive ambiguity widgets. These widgets allow the user to resolve ambiguities by surfacing system decisions at the point where the ambiguity matters. Corrections are stored as constraints and influence subsequent queries. We have implemented these ideas in a system, DataTone. In a comparative study, we find that DataTone is easy to learn and lets users ask questions without worrying about syntax and proper question form.

More and more people are collecting, organizing, and interpreting health data through off-the-shelf products such as Apple's Health Kit. People use these systems to collect steps taken, calories ingested, etc. These ecosystems also support the collection of physiological data. For example, users collect heart rate data during exercise, and even electro dermal activity data to help detect the onset of seizures. Analyzing physiological data, however, and relating it to specific behaviors or events, is challenging. In this paper, we present an 11-week, multi-session, participatory design case study, identify challenges in analyzing physiological and behavior data, and present BEDA, an analytics tool we developed to mitigate the challenges. The two primary data analysis challenges include: (1) interfacing multiple software programs required for capturing and analyzing the different data sources, and (2) extending the limited data analysis functionality within and across these software programs to support a wide range of physiological data analyses. BEDA resolves the fragmented analysis pipeline by integrating closely-related analysis tasks into a common interface. It also addresses the extensibility problem by integrating scripts that apply any custom or publicly-available function written in MATLAB or R. These scripts extend basic analytic capability, provide the analytic bridge between physiological and behavior data, and incorporate machine learning algorithms to highlight behaviors associated with physiological data. BEDA's capabilities mitigated the challenges of signal analysis and fragmented data sources, and motivated behavioral scientists to combine physiological measures with behavioral analysis. Although we developed this tool for a domain-specific case study, the use of the tool can be generalized to analyze any time-based data source or sources.

Social reminder interfaces on social networking sites (SNSs), such as the Facebook birthday reminder, make sending a congratulatory message easier than ever. However, the lower cost in time and effort can also devalue a simple message, and one-click congratulations may be criticized as impersonal. Nevertheless, they are still widely used. In this paper, we investigate how people find value in short congratulatory birthday messages on Facebook despite the criticism. We conducted interviews with 17 participants with the aid of a reflective prompting tool developed to aggregate participants’ previous birthday posts. Participants found the most value in personalized birthday posts, posts for reconnecting with dormant ties, and in the presentation of public affirmation posts for their imagined audience. We use signaling theory to interpret our findings and explain how a social reminder interface that lowers the cost in time for sending congratulatory messages can be both beneficial and problematic.

Welcome to this issue of the Proceedings of the ACM on Human-Computer Interaction, which will focus on contributions from the research community Computer-Supported Cooperative Work and Social Computing (CSCW). This diverse research community explores how different types of social groups affect, and are affected by, information and communication technology. The topics explored by this community can include social media use, crowdsourcing and micro-work, societal effects of computing, and much more. Like many other HCI communities, CSCW approaches these topics with a broad range of scientific techniques, theoretical perspectives and technology platforms. The call for papers for this issue on CSCW attracted 385 submissions, from Asia, Canada, Australia, Europe, Africa, and the United States. After the first round of reviewing, 207 (54%) papers were invited to the Revise and Resubmit phase. The editorial committee worked hard over August 2017 to arrive at final decisions, with a Virtual Committee meeting held to discuss those papers that needed collective deliberation. In the end, 105 papers (27%) were accepted. This issue exists because of the dedicated volunteer effort of 101 senior editors who served as Associate Chairs (ACs), and 885 expert reviewers to ensure high quality and insightful reviews for all papers in both rounds. Reviewers and committee members were kept constant for papers that submitted to both rounds. Senior members of the editorial group also helped shepherd some papers, reflecting the deep commitment of this research community. We are excited by the compelling and thought-provoking work that resulted in this PACMHCI CSCW issue and look forward to equally high quality submissions for the next submission cycle from this research community in the Spring of 2018. For those interested in this area, this group holds their next annual conference November 3-7, 2018 in New York City's Hudson River (Jersey City). That conference will provide many opportunities to share ideas with other researchers and practitioners from institutions around the world.

Popular online social networks (OSN) generate hundreds of terabytes of new data per day and connect millions of users. To help users cope with the immense scale and influx of new information, OSNs provide a search functionality. However, most of the search engines in OSNs today only support keyword queries and provide basic faceted search capabilities overlooking serendipitous network exploration and search for relationships between OSN entities. This results in siloed information and a limited search space. In 2013 Facebook introduced its innovative Graph Search product with the goal to take the OSN search experience to the next level and facilitate exploration of the Facebook Graph beyond the first degree. In this paper we explore people search on Facebook by analyzing an anonymized social graph, anonymized user profiles, and large scale anonymized query logs generated by users of Facebook Graph Search. We uncover numerous insights about people search across several demographics. We find that named entity and structured queries complement each other across one's duration on Facebook, that females search for people proportionately more than males, and that users submit more queries as they gain more friends. We introduce the concept of a lift predicate and highlight how a graph distance varies with the search goal. Based on these insights, we present a set of design implications to guide the research and development of the OSN search in the future.

LinkedIn is the world's largest professional network, with over 300 million members. One of the primary activities on the site is people search, for which LinkedIn members are both the users and the corpus. This paper presents insights about people search behavior on LinkedIn, based on a log analysis and a user study. In particular, it examines the role that network distance plays in name searches and non-name searches. For name searches, users primarily click on only one of the results, and closer network distance leads to higher click-through rates. In contrast, for non-name searches, users are more likely to click on multiple results that are not in their existing connections, but with whom they have shared connections. The results show that, while network distance contributes significantly to LinkedIn search engagement in general, its role varies dramatically depending on the type of search query.

Managing friendship relationships is challenging due to the growing number of people in online social networks (OSNs). While grouping friends sometimes mitigates this challenge, the burden of manual grouping still prevents OSNs users to create groups widely for privacy control, selective sharing and filtering. In this paper, we present an automated friend grouping tool which utilizes three different clustering algorithms to create groups from Facebook friendship networks. By conducting 18 semi-structured interviews, we investigated the advantages and disadvantages of automated friend grouping in OSNs.

Sound, especially speech, is ephemeral. It is a high-speed, ordered, multichannel stream that plays for a time, and leaves shadows of its presence in our memories. When we communicate, we exchange semantic, expressive, and relational messages. Most of our communicative power lies outside semantics, yet these expressive and relational exchanges are underexplored. We and others have experimented with visual representations of speech, yet little is known about the interpretability, usability, and efficacy of the visualizations; here we focus on interpretability. We provide a system for expressive vocal analysis, new voice visualizations which map vocal parameters to different designs, and a study focusing on the interpretability of the resulting voice visualizations.

Examples are very important in design, but existing tools for design example search still do not cover many cases. For instance, long tail queries containing subtle and subjective design concepts, like "calm and quiet", "elegant", "dark background with a hint of color to make it less boring", are poorly supported. This is mainly due to the inherent complexity of the task, which so far has been tackled only algorithmically using general image search techniques. We propose a powerful new approach based on crowdsourcing, which complements existing algorithmic approaches and addresses their shortcomings. Out of many explored crowdsourcing configurations we found that (1) a design need should be represented via several query images and (2) AMT crowd workers should assess a query-specific relevance of a candidate example from a pre-built design collection. To test the utility of our approach, we compared it with Google Images in a query-by-example mode. Based on feedback from expert designers, the crowd selects more relevant design examples.

Managing friendship relationships in social media is challenging due to the growing number of people in online social networks (OSNs). To deal with this challenge, OSNs’ users may rely on manually grouping friends with personally meaningful labels. However, manual grouping can become burdensome when users have to create multiple groups for various purposes such as privacy control, selective sharing, and filtering of content. More recently, recommendation-based grouping tools such as Facebook smart lists have been proposed to address this concern. In these tools, users must verify every single friend suggestion. This can hinder users’ adoption when creating large content sharing groups. In this paper, we proposed an automated friend grouping tool that applies three clustering algorithms on a Facebook friendship network to create groups of friends. Our goal was to uncover which algorithms were better suited for social network groupings and how these algorithms could be integrated into a grouping interface. In a series of semi-structured interviews, we asked people to evaluate and modify the groupings created by each algorithm in our interface. We observed an overwhelming consensus among the participants in preferring this automated grouping approach to existing recommendation-based techniques such as Facebook smart lists. We also discovered that the automation created a significant bias in the final modified groups. Finally, we found that existing group scoring metrics do not translate well to OSN groupings–new metrics are needed. Based on these findings, we conclude with several design recommendations to improve automated friend grouping approaches in OSNs.

Proliferation of mobile smartphones has opened up possibilities of using crowd-sourcing to gather data from and so monitor large crowds. However, depending on the size of the crowd, current solutions either put unpredictable stress on the infrastructure and energy-constrained smartphones or do not capture the crowd behavior accurately. In response, we present CrowdWatch, a scalable, distributed and energy-efficient crowd-sourcing framework. CrowdWatch achieves its goal through off-loading some of the processing to the devices and establishing a hierarchy of participants by exploiting devices with multiple radios (i.e. WiFi (high-power) and BlueTooth (low-power)). CrowdWatch can outperform traditional crowd-sourcing frameworks by reducing the stress on the infrastructures to 10% of that of a traditional crowd-sourcing solution, while only requiring each phone to use their Wi-Fi radios 15% of the time in a dense environment.

Aiming to improve user experience for a job search engine, in this paper we propose an idea to switch from query-biased snippets used by most web search engines to rich structured snippets associated with the main sections of a job posting page, which are more appropriate for job search due to specific user needs and the structure of job pages. We present a very simple yet actionable approach to generate such snippets in an unsupervised way. The advantages of the proposed approach are two-fold: it doesn't require manual annotation and therefore can be easily deployed to many languages, which is a desirable property for a job search engine operating internationally; it fuses naturally with the trend towards Mobile Web where the content needs to be optimized for small screen devices and informativeness.

We propose using discovery-based learning games to teach people how to use complex software. Specifically, we developed Jigsaw, a learning game that asks players to solve virtual jigsaw puzzles using tools in Adobe Photoshop. We conducted an eleven-person lab study of the prototype, and found the game to be an effective learning medium that can complement demonstration-based tutorials. Not only did the participants learn about new tools and techniques while actively solving the puzzles in Jigsaw, but they also recalled techniques that they had learned previously but had forgotten.

Real-time collaborative visualization of conversation affects small group dynamics and encourages balanced participation. Visual feedback plays the role of a gentle moderator encouraging a speaker to yield the floor. To better evaluate the effect of visual feedback on collaboration, we purposefully distorted the apparent balance in the Conversation Clock, a shared visualization of conversation. We present a pilot study examining various distortion strategies followed by a more in depth study applying distortion to discussion. Our results indicate that participants will accept some significantly distorted visualizations as an accurate representation of conversation, however, distorted feedback provides only minimal impact on conversational balance. These findings suggest the mechanism in visualization that drives individuals towards balanced conversation is not solely influenced by the visual feedback and remains an open question for future work.

The impact of living in a world that does not understand your impairment can be frustrating and a daunting task. Consider how an individual would feel if their family, friends, or doctors did not understand or were not even empathetic to daily struggles brought on by an acquired language disorder such as Aphasia. This work seeks to shed new light on aphasia by creating an instant message client which emulates the effects of aphasia. The goal of this new system is to raise awareness, teach, and increase empathy for caregivers, family members, and doctors/therapists who work with this population on a daily basis.