Technology provides youth (ages 10--17) with near-constant opportunities for learning, communication, and self-expression. It can also expose them to technology-facilitated abuse: harassment, coercion, fraud, and more. The ability of youth to navigate such abuse is crucial for their well-being and development. A recent advisory by the U.S. Surgeon General called for better support of youth, including that youth should ''reach out for help.'' However, little is known about how youth seek help or otherwise cope with technology-facilitated abuse. Through a qualitative study in the U.S., we examine how youth engage in self-reliance, seek help from others, and how others seek help on a youth's behalf. We discuss these strategies and outline opportunities for how the HCI community can better support youth who experience technology-facilitated abuse.

The seamless integration of technology into the lives of youth has raised concerns about their digital safety. While prior work has explored youth experiences with physical, sexual, and emotional threats—such as bullying and trafficking—a comprehensive and in-depth understanding of the myriad threats that youth experience is needed. By synthesizing the perspectives of 36 youth and 65 adult participants from the U.S., we provide an overview of today’s complex digital-safety landscape. We describe attacks youth experienced, how these moved across platforms and into the physical world, and the resulting harms. We also describe protective practices the youth and the adults who support them took to prevent, mitigate, and recover from attacks, and key barriers to doing this effectively. Our findings provide a broad perspective to help improve digital safety for youth and set directions for future work.

This paper addresses calls for more research on privacy in the gig economy across a range of work platforms. To understand privacy risks, behaviors, and consequences from the perspective of workers, we analyzed workers’ posts about privacy and surveillance from 12 Reddit forums representing four main types of work (crowdwork, freelancing, ridesharing, and delivery). We found that workers perceive both platform companies and customers as sources of unnecessary and opaque data collection and surveillance that can threaten their privacy, safety, and economic outcomes. Workers also engage in many risk mitigation strategies, including self-protective surveillance behaviors such as video recording themselves and customers, as a costly but necessary response to power imbalances created by surveillance. Based on our multi-platform analysis, we present a guiding set of questions that workers, designers, and researchers can use to assess the privacy implications of current and future gig work platforms.

Communication tools such as email facilitate communication and collaboration between speakers of different languages, who use two primary strategies-English as a common language and machine translation (MT) tools-to help them overcome language barriers. However, each of these communication strategies creates its own challenges for cross-lingual communication. In this paper, we compare how people's interpretations of an email sender's social intention, and their evaluation of the email and the senders, differ when using a common language versus MT in email communication. We conducted an online experiment in which monolingual native English speakers read and rated request emails written by native English speakers, emails written by bilingual Chinese speakers in English, and emails written in Chinese then machine-translated into English. We found that participants interpreted the social intentions of the email sender less accurately for machine-translated emails than for emails written by non-native speakers in English. Participants also rated the senders and emails less positively overall for machine-translated emails compared to emails written by non-native speakers in English. Based on these findings, we suggest design possibilities that could better aid multilingual communication.

Little is known about whether and how workers with disabilities participate in the many on-demand labor platforms that make up the growing gig economy. Understanding disabled gig workers' experiences is a vital step toward developing inclusive and equitable labor platforms. Through interviews with 24 disabled gig workers and observational fieldwork, we present a rich, in-depth picture of the opportunities and challenges presented by four main types of gig work (ridesharing, delivery, crowdwork, and freelancing) for workers with a wide range of disabilities. We identify a key tension: gig work can be a vital source of needed income for workers who have been excluded from traditional workplaces, but at the same time, the structure of gig platforms present workers with a host of new disability-related challenges, including around algorithmic control and performance evaluation. We discuss how this tension plays out in terms of how workers engage in gig work and protect themselves from risk. We also call attention to how many workers can face complicated, intersectional challenges based on multiple marginalized identities in addition to disability, such as race, gender, sexual orientation, and socioeconomic status. Our work contributes to research on the gig economy by centering the perspectives of workers who are marginalized based on disability and other identities. We show how workers face several penalties based on disability, including shouldering extensive invisible labor to mitigate the challenges they face. Based on our interviews, we suggest several ways that on-demand labor platforms can be designed to be more inclusive of disability, including how to improve the accessibility of various tasks while mitigating the discrimination and negative interactions faced by disabled workers.

Tasks on crowdsourcing platforms such as Amazon Mechanical Turk often request workers' personal information, raising privacy risks that may be exacerbated by requester-worker power dynamics. We interviewed 14 workers to understand how they navigate these risks. We found that Turkers' decisions to provide personal information during tasks were based on evaluations of the pay rate, the requester, the purpose, and the perceived sensitivity of the request. Participants also engaged in multiple privacy-protective behaviors, such as abandoning tasks or providing inaccurate data, though there were costs associated with these behaviors, such as wasted time and risk of rejection. Finally, their privacy concerns and practices evolved as they learned about both the platform and worker-designed tools and forums. These findings deepen our understanding of both privacy decision-making and invisible labor in paid crowdsourcing, and emphasize a general need to understand how privacy stances change over time.

The goal of this paper is to investigate how computational tools to annotate communication can support multilingual sense-making on social media. We conducted a field study of SenseTrans, a browser extension that uses sentiment analysis and named entity extraction techniques to annotate Facebook posts with emotional and contextual information. Interviews with 18 participants who used SenseTrans in their Facebook newsfeed for two weeks suggest that the annotations often supported sensemaking by providing additional information they could use to get a quick gist of the posts or to supplement their own interpretations. Participants varied in the extent to which they were motivated to evaluate the credibility of and form mental models of how the annotations were generated, which shaped how they utilized the annotations for sensemaking. Our findings demonstrate the value of designing to support cross-lingual communication and inform design implications for intelligent tools that support communication and sensemaking.

Although many social media sites now provide machine translation (MT) for foreign language posts, translation of a post may not suffice to support understanding of, and engagement with, that post. We present SenseTrans, a tool that provides emotional and contextual annotations generated by natural language analysis in addition to machine translation. We evaluated SenseTrans in a laboratory experiment in which native English speakers browsed five Facebook profiles in foreign languages. One group used the SenseTrans interface while the other group used MT alone. Participants using SenseTrans reported significantly greater understanding of the posts, and greater willingness to engage with the posts. However, no additional cognitive load was associated with using an interface that provided more information. These results provide promising support for the idea of using computational tools to annotate communication to support multilingual sense making and interaction on social media.

In this paper, we study online privacy lies: lies primarily aimed at protecting privacy. Going beyond privacy lenses that focus on privacy concerns or cost/benefit analyses, we explore how contextual factors, motivations, and individual-level characteristics affect lying behavior through a 356-person survey. We find that statistical models to predict privacy lies that include attitudes about lying, use of other privacy-protective behaviors (PPBs), and perceived control over information improve on models based solely on self-expressed privacy concerns. Based on a thematic analysis of open-ended responses, we find that the decision to tell privacy lies stems from a range of concerns, serves multiple privacy goals, and is influenced by the context of the interaction and attitudes about the morality and necessity of lying. Together, our results point to the need for conceptualizations of privacy lies-and PPBs more broadly-that account for multiple goals, perceived control over data, contextual factors, and attitudes about PPBs.

Public concern related to a policy may span a range of topics. As a result, policy discussions struggle to deeply examine any one topic before moving to the next. In policy deliberation research, this is referred to as a problem of topical coherence. In an experiment, we curated the comments in a policy discussion to prioritize arguments for or against a policy proposal, and examined how this curation and participants' initial positions of support or opposition to the policy affected the coherence of their contributions to existing topics. We found an asymmetric interaction between participants' initial positions and comment curation: participants with different initial positions had unequal reactions to curation that foregrounded comments with which they disagreed. This asymmetry implies that the factors underlying coherence are more nuanced than prioritizing participants' agreement or disagreement. We discuss how this finding relates to curating for coherent disagreement, and for curation more generally in deliberative processes.

Inspired by policy deliberation methods and iterative writing in crowdsourcing, we developed and evaluated a task in which newcomers to an online policy discussion, before entering the discussion, generate prompts that encourage existing commenters to engage with each other. In an experiment with 453 Amazon Mechanical Turk (AMT) crowd workers, we found that newcomers can often craft acceptable prompts, especially when given guidance on prompt-writing and balanced opinions between the comments they synthesize. However, crafting these prompts had little effect on the quality of comments they posted to a simulated discussion forum following the prompt task, as measured by the reasoning and topic coherence of comments. Our results inform best practices and pose questions for the design of discussion systems, both in general and for online policy discussion in particular.

The design of most instant messaging (IM) tools focuses on exchanging content as the meaning and process of communication, a convention that equates content with meaning in a way counter to modern theories of interpersonal communication. We critique these conventions in designing a mobile messaging application, BubbleQ, which de-emphasizes message content and defamiliarizes interaction flows by forbidding users to send content directly. Instead, users send empty "bubbles", blank messages that recipients can fill and return to the sender. Messages are also presented not as per-user threads, but as an aggregation of bubbles across all conversational partners; they are also given a physical form to interact with to further violate common IM design conventions. Interviews with 28 college students who used BubbleQ for two weeks show that these design twists made room for negotiating other meanings, supported playful, mundane interaction, and provided resources for deepening relationships. Further, the aggregate bubbles view led people to reflect holistically on interactions. Together, these findings suggest that IM designers should spend more time addressing the meanings, embodiments, embedding, and cost of messages.

Over the last decade, the idea of a “quantified self” has become increasingly well-known (Swan, 2013). The main premise is to collect and reflect on data about oneself in order to gain new insights...

Online crowd labor markets often address issues of risk and mistrust between employers and employees from the employers' perspective, but less often from that of employees. Based on 437 comments posted by crowd workers (Turkers) on the Amazon Mechanical Turk (AMT) participation agreement, we identified work rejection as a major risk that Turkers experience. Unfair rejections can result from poorly-designed tasks, unclear instructions, technical errors, and malicious Requesters. Because the AMT policy and platform provide little recourse to Turkers, they adopt strategies to minimize risk: avoiding new and known bad Requesters, sharing information with other Turkers, and choosing low-risk tasks. Through a series of ideas inspired by these findings-including notifying Turkers and Requesters of a broken task, returning rejected work to Turkers for repair, and providing collective dispute resolution mechanisms-we argue that making reducing risk and building trust a first-class design goal can lead to solutions that improve outcomes around rejected work for all parties in online labor markets.

Often, attention to “community” focuses on motivating core members or helping newcomers become regulars. However, much of the traffic to online communities comes from people who visit only briefly. We hypothesize that their personal characteristics, design elements of the site, and others' activity all affect the contributions these "one-timers" make. We present the results from an experiment asking Amazon Mechanical Turk (“AMT”) workers to comment on the AMT participation agreement in a discussion forum. One-timers with stronger ties to other Turkers or feelings of trust for Amazon are more likely to leave more --- but shorter and less relevant --- comments, while those with higher self-efficacy leave longer and more relevant comments. The phrasing of prompts also matters; a general appeal for personally-reflective contributions leads to comments that are less relevant to community discussion topics. Finally, activity matters too; synchronous activity begets responses, while pre-existing content tends to suppress them. These findings suggest design moves that can help communities harness this “long tail” of contribution.

Many online social networks thrive on automatic sharing of friends' activities to a user through activity feeds, which may influence the user's next actions. However, identifying such social influence is tricky because these activities are simultaneously impacted by influence and homophily. We propose a statistical procedure that uses commonly available network and observational data about people's actions to estimate the extent of copy-influence---mimicking others' actions that appear in a feed. We assume that non-friends don't influence users; thus, comparing how a user's activity correlates with friends versus non-friends who have similar preferences can help tease out the effect of copy-influence.

Unlike most social media, where automatic archiving of data is the default, Snapchat defaults to ephemerality: deleting content shortly after it is viewed by a receiver. Interviews with 25 Snapchat users show that ephemerality plays a key role in shaping their practices. Along with friend-adding features that facilitate a network of mostly close relations, default deletion affords everyday, mundane talk and reduces self-consciousness while encouraging playful interaction. Further, although receivers can save content through screenshots, senders are notified; this selective saving with notification supports complex information norms that preserve the feel of ephemeral communication while supporting the capture of meaningful content. This dance of giving and taking, sharing and showing, and agency for both senders and receivers provides the basis for a rich design space of mechanisms, levels, and domains for ephemerality.

Our body clock causes considerable variations in our behavioral, mental, and physical processes, including alertness, throughout the day. While much research has studied technology usage patterns, the potential impact of underlying biological processes on these patterns is under-explored. Using data from 20 participants over 40 days, this paper presents the first study to connect patterns of mobile application usage with these contributing biological factors. Among other results, we find that usage patterns vary for individuals with different body clock types, that usage correlates with rhythms of alertness, that app use features such as duration and switching can distinguish periods of low and high alertness, and that app use reflects sleep interruptions as well as sleep duration. We conclude by discussing how our findings inform the design of biologically-friendly technology that can better support personal rhythms of performance.

Online communities provide a fertile ground for analyzing people's behavior and improving our understanding of social processes. Because both people and communities change over time, we argue that analyses of these communities that take time into account will lead to deeper and more accurate results. Using Reddit as an example, we study the evolution of users based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between users---yearly cohorts---we find wide differences in people's behavior, including comment activity, effort, and survival. Further, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson's Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward.

OBJECTIVE: To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS: Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS: Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION: Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION: This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

People often share emotions with others in order to manage their emotional experiences. We investigate how social media properties such as visibility and directedness affect how people share emotions in Facebook, and their satisfaction after doing so. 141 participants rated 1,628 of their own recent status updates, posts they made on others' timelines, and private messages they sent for intensity, valence, personal relevance, and overall satisfaction felt after sharing each message. For network-visible channels-status updates and posts on others' timelines-they also rated their satisfaction with replies they received. People shared differently between channels, with more intense and negative emotions in private messages. People felt more satisfied after sharing more positive emotions in all channels and after sharing more personally relevant emotions in network-visible channels. Finally, people's overall satisfaction after sharing emotions in network-visible channels is strongly tied to their reply satisfaction. Quality of replies, not just quantity, matters, suggesting the need for designs that help people receive valuable responses to their shared emotions.

Machine translation (MT) creates both opportunities and challenges for multilingual collaboration: While MT enables collaborators to communicate via their native languages, it can introduce errors that make communication difficult. In the current paper, we examine whether displaying two alternative translations for each message will improve conversational grounding and task performance. We conducted a laboratory experiment in which monolingual native English speakers collaborated with bilingual native Mandarin speakers on a map navigation task. Each dyad performed the task in one of three communication conditions: MT with single output, MT with two outputs, and English as a common language. Dyads given two translations for each message communicated more efficiently, and performed better on the task, than dyads given one translation. Our findings show the value of providing multiple translations in multilingual collaboration, and suggest design features of future MT-based collaboration tools.

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.

In modeling social interaction online, it is important to understand when people are reacting to each other. Many systems have explicit indicators of replies, such as threading in discussion forums or replies and retweets in Twitter. However, it is likely these explicit indicators capture only part of people's reactions to each other, thus, computational social science approaches that use them to infer relationships or influence are likely to miss the mark. This paper explores the problem of detecting non-explicit responses, presenting a new approach that uses tf-idf similarity between a user's own tweets and recent tweets by people they follow. Based on a month's worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion.

When crime analysts collaborate to solve crime cases, they need to share insights in order to connect the clues, identify a pattern, and attribute the crime to the right culprit. We designed a collaborative analysis tool to explore the value of implicitly sharing insights and notes, without requiring analysts to explicitly push information or request it from each other. In an experiment, pairs of remote individuals played the role of crime analysts solving a set of serial killer crimes with both partners having some, but not all, relevant clues. When implicit sharing of notes was available, participants remembered more clues related to detecting the serial killer, and they perceived the tool as more useful compared to when implicit sharing was not available.

We propose to improve real-time communication between people who do not share a common language by foregrounding potential problems in machine translation. We developed a prototype chat tool that displays two parallel translations of each chat turn, with the thought that comparing the translations might both highlight problems and provide resources for resolving them. We conducted a user study to investigate how people use and like such an interface compared to a standard one-translation interface. On balance, users preferred two translations to one, using them to both notice differences and infer meaning from uncertain translations, with no increase in workload. This suggests that this interface may help improve cross-lingual communication in practical applications and lays the groundwork for a larger design space around systems that highlight possible errors to support communication.

Traditional communication tools tend to make their presence known, e.g., "when my collaborators and I are using IM to discuss our work, how could we not realize the actual presence of IM?" In the case of machine translation (MT) mediated collaborations, however, the absence or presence of MT is not obvious. English sentences with poor grammar can result from both a partner's lack of fluency and errors in the MT process. We hypothesize that partners' attributions about the source of the errors affects their collaboration experience. To test this hypothesis, we conducted a laboratory experiment in which monolingual native English speaking participants collaborated with bilingual native-Mandarin speakers on a map navigation task. Participants were randomly assigned into a 2 (beliefs about MT: absence vs. presence) by 2 (actual mediation of MT: absence vs. presence) experiment design. Beliefs about presence of MT significantly impacted the collaboration experience, opening new opportunities for both research and design around MT-mediated collaborations.

Machine translation (MT) has the potential to allow members of multilingual organizations to interact via their own native languages, but issues with the quality of MT output have made it difficult to realize this potential. We hypothesized that highlighting keywords in MT output might make it easier for people to overlook translation errors and focus on what was intended by the message. To test this hypothesis, we conducted a laboratory experiment in which native English speakers interacted with a Mandarin-speaking confederate using machine translation. Participants performed three brainstorming tasks, under each of three conditions: no highlighting, keyword highlighting, and random highlighting. Our results indicated that people consider the identical messages clearer and less distracting when the keywords in the message are highlighted. Keyword highlighting also improved subjective impressions of the partner and the quality of the collaboration. These findings inform the design of future communication tools to support multilingual communications.

We present an alternative approach to the design of personal informatics systems: instead of motivating people to examine their own behaviors, this approach promotes awareness of and reflection on the infrastructures behind personal informatics and the modes of engagement that they promote. Specifically, this paper presents an interface that displays personal web browsing data. The interface aims to reveal underlying infrastructure using several methods: drawing attention to the scope of mined data by displaying deliberately selected sensitive data, using purposeful malfunction as a way to encourage reverse engineering, and challenging normative expectations around data mining by displaying information in unconventional ways. Qualitative results from a two-week deployment show that these strategies can raise people's awareness about data mining, promote efficacy and control over personal data, and inspire reflection on the goals and assumptions embedded in infrastructures for personal data analytics.

The growing use of social media means that an increasing amount of people's lives are visible online. We draw from Goffman's theatrical metaphor and Hogan's exhibition approach to explore how people manage their personal collection of social media data over time. We conducted a qualitative study of 13 participants to reveal their day-to-day decision-making about producing and curating digital traces on Facebook. Their goals and strategies showed that people experience the Facebook platform as consisting of three different functional regions: a performance region for managing recent data and impression management, an exhibition region for longer term presentation of self-image, and a personal region for archiving meaningful facets of life. Further, users' need for presenting and archiving data in these three regions is mediated by temporality. These findings trigger a discussion of how to design social media that support these dynamic and sometimes conflicting needs.

Diversity among members of international teams can be a valuable source of novel ideas. However, to reap these benefits, groups need to overcome communication barriers that stem from differences in members' native languages. We compare two strategies for overcoming these barriers: the use of English as a common language, and the use of machine translation (MT) tools that allow each person to communicate in his or her own native language. Dyads consisting of one English-speaking American and one native Mandarin-speaking Chinese participant exchanged ideas to perform brainstorming tasks, either through English or using MT. We found that MT helped the non-native English speakers produce ideas but that both native and non-native English speakers viewed MT-mediated messages as less comprehensible than English messages. The findings suggest it can be effective to support cross-lingual communication with asymmetric design, using MT technology to help people produce messages in their native languages, while leaving incoming messages untranslated and leveraging people's second language proficiency for comprehension.

Informal communication in organizations has many benefits, but people who are not native speakers of the organization's common language may find it hard to interact informally. In an interview study of nine native English-speaking and 33 non-native English-speaking students at a large U.S. university, we explore how native language shapes patterns of informal interaction. We found that non-native speakers generally preferred interacting informally with fellow speakers of their own native language as opposed to native English speakers, which hinders communication and collaboration between groups. Three factors led to this "clustering" effect: issues of common ground, feelings of social obligation to other speakers of one's native language, and desires to build social networks within a language group. Four factors led to greater motivation for cross-language interaction: a desire to build bridging capital, physical proximity, one-on-one or small-group interaction, and an established work relationship. The findings suggest ways that communication tools might reduce barriers to informal interaction between speakers of different native languages.

In this paper we address the problem of developing actionable quality models for Wikipedia, models whose features directly suggest strategies for improving the quality of a given article. We first survey the literature in order to understand the notion of article quality in the context of Wikipedia and existing approaches to automatically assess article quality. We then develop classification models with varying combinations of more or less actionable features, and find that a model that only contains clearly actionable features delivers solid performance. Lastly we discuss the implications of these results in terms of how they can help improve the quality of articles across Wikipedia.

Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.

Romantic partners face issues of relational development including managing information privacy, tension between individual and relational needs, and accountability to existing friends. Prior work suggests that affordances of social media might highlight and shape these tensions; to explore this, we asked 20 people to reflect daily for two weeks on feelings and decisions around their own and others' Facebook use related to their relationships. Most generally, we find that tensions arise when romantic partners must manage multiple relationships simultaneously because Facebook audiences are so present and so varied. People also engage in subtle negotiation around and appropriation of Facebook's features to accomplish both personal and relational goals. By capturing both why people make these decisions and how Facebook's affordances support them, we expect our findings to generalize to many other social media tools and to inform theorizing about how these tools affect relational development.

Inspired by conversational visualization tools and the increasing enactment of relationships in social media, we examine how people reflect on friendships and how social data and conversation may affect this. We asked 28 people to reflect on their relationship with a close friend either alone, alone but with access to Facebook's "See Friendship" page, or with the friend using their See Friendship page. Observation and interviews revealed a rich array of practices around why, when, and how people reflect on friendships; that both friends and data make reflection more positive, more focused, and more fun; that those are not necessarily good things; and that third parties are a common theme. These findings suggest a number of design considerations, including supporting different types of reflection, aligning the interface with important moments and content useful for reflection, and carefully considering the fidelity of the visualization and data presented.

Reminiscing is a valuable activity throughout the lifespan, helping people establish and maintain their identities and their relationships. Much of this happens in an everyday way, with reminiscing arising naturally out of one's experiences, thoughts, and conversations. In this article we describe work around Pensieve, a tool to support everyday, spontaneous, individual reminiscing through memory triggers—e-mailed reminders that contain snippets from content one has previously created in social media or generic questions that encourage people to reflect on their past. Through a combination of interviews, questionnaires, design activities, and a long-term deployment of Pensieve, we demonstrate the potential value of social media content such as Facebook wall posts and status updates for supporting reminiscence, the utility of systems that support spontaneous reminiscing and writing about the past, the importance of reminders to both reminiscing and lifelogging systems, and insights into people's current pr...

This paper argues for evaluating the impact of persuasive systems on users beyond metrics that focus on system usage, based on an interview study of 16 Wii Fit users. While exploring their experiences and reasons for abandoning the system, two main themes emerged: the tension between Wii Fit as a fitness tool and a game, and ways participants reacted to the system's feedback about their weight and performance. Some participants used Wii Fit as a "gateway fitness" tool, moving beyond it to other fitness routines. Additionally, some users had significant emotional reactions to the Wii Fitts feedback. We argue that these 'side effects' are crucial considerations for the design and long-term evaluation of persuasive technologies.

Group brainstorming, or collaboratively generating ideas through idea sharing, demands diverse contributions to spark more ideas and improve creativity. One approach to supporting group brainstorming is to introduce conceptual diversity. In this study, we evaluate the effects of two sources of diversity on group brainstorming: cultural differences internal to multicultural groups and pictures related to the conversation retrieved by a computer agent. The pictures generally enhanced performance as measured by both originality and diversity of ideas. The pictures also helped to convert cultural diversity into a creative outcome, the diversity of ideas generated. We argue that with appropriate technology mediation, cultural diversity may be used strategically to enhance task outcomes.

Reminiscing is a valuable activity that people of all ages spontaneously and informally partake in as part of their everyday lives. This paper discusses the design and use of Pensieve, a system that supports everyday reminiscence by emailing memory triggers to people that contain either social media content they previously created on third-party websites or text prompts about common life experiences. We discuss how the literature on reminiscence informed Pensieve's design, then analyze data from 91 users over five months. We find that people value spontaneous reminders to reminisce as well as the ability to write about their reminiscing. Shorter, more general triggers draw more responses, as do triggers containing people's own photos-although responses to photos tended to contain more metadata elements than storytelling elements. We compare these results to data from a second, Pensieve-like system developed for Facebook, and suggest a number of important aspects to consider for both designers and researchers around technology and reminiscence.

When experts participate in collaborative systems, tension may arise between them and novice contributors. In particular, when experts perceive novices as a bother or a threat, the experts may express territoriality: behaviors communicating ownership of a target of interest. In this paper, we describe the results of a user study of a mobile social tagging system deployed within a museum gallery to a group of novices and experts collaboratively tagging part of the collection. We observed that experts express greater feelings of ownership towards their contributions to the system and the museum in general. Experts were more likely than novices to participate at higher rates and to negatively evaluate contributions made by others. We suggest a number of design strategies to balance experts' expressions of territoriality so as to motivate their participation while discouraging exclusionary behaviors.

Creativity is central to much human problem solving and innovation. Brainstorming processes attempt to leverage group creativity, but group dynamics sometimes limit their utility. We present IdeaExpander, a tool to support group brainstorming by intelligently selecting pictorial stimuli based on the group's conversation The design is based on theories of how perception, thinking, and communication interact; a pilot study (N=16) suggests that it increases individuals' idea production and that people value it.

While there has been significant research around aspects of tagging systems such as the vocabulary people use and the reasons they tag, there has been little focus on the design of the tagging interface itself. This paper discusses how kultagg, a ludic interface that includes the ability to color tags and place them directly on images, affect people's behavior and attitudes toward tagging. We conducted interviews with 10 people, asking them to use and reflect on kultagg. Color plays a significant role in enhancing a user's interest and enjoyment in tagging and has uses from self-expression to organization. People appreciated on-image tagging for its personal nature, ease of use, and specificity, although these tags tended to be less abstract and holistic than tags created in a more typical interface. Participants' generally positive response to kultagg suggests that including ludic elements in task-oriented domains is useful in creating rich, expressive systems.

Designers of mobile, social systems must carefully think about how to help their users manage spatial, semantic, and social modes of navigation. Here, we describe our deployment of MobiTags, a system to help museum visitors interact with a collection of "open storage" exhibits, those where the museum provides little curatorial information. MobiTags integrates social tagging, art information, and a map to support navigation and collaborative curation of these open storage collections. We studied 23 people's use of MobiTags in a local museum, combining interview data with device use logs and tracking of people's movements to understand how MobiTags affected their navigation and experience in the museum. Despite a lack of social cues, people feel a strong sense of social presence--and social pressure--through seeing others' tags. The tight coupling of tags, item information, and map features also supported a rich set of practices around these modes of navigation.

While most collaboration technologies are concerned with supporting particular tasks such as workflows or meetings, many work groups do not have the teamwork skills essential to effective collaboration. One way to improve teamwork is to provide dynamic feedback generated by automated analyses of behavior, such as language use. Such feedback can lead members to reflect on and subsequently improve their collaborative behavior, but might also distract from the task at hand. We have experimented with GroupMeter - a chat-based system that presents visual feedback on team members' language use. Feedback on proportion of agreement words and overall word count was presented using two different designs. When receiving feedback, teams in our study expressed more agreement in their conversations and reported greater focus on language use as compared to when not receiving feedback. This suggests that automated, real-time linguistic feedback can elicit behavioral changes, offering opportunities for future research.

Territoriality, the expression of ownership towards an object, can emerge when social actors occupy a shared social space. In the case of Wikipedia, the prevailing cultural norm is one that warns against ownership of one's work. However, we observe the emergence of territoriality in online space with respect to a subset of articles that have been tagged with the Maintained template through a qualitative study of 15 editors who have self-designated as Maintainers. Our participants communicated ownership, demarcated boundaries and asserted their control over artifacts for the sake of quality by appropriating existing features of Wikipedia. We then suggest design strategies to support these behaviors in the proper context within collaborative authoring systems more generally.

Technologies in museums often support learning goals, providing information about exhibits. However, museum visitors also desire meaningful experiences and enjoy the social aspects of museum-going, values ignored by most museum technologies. We present ArtLinks, a visualization with three goals: helping visitors make connections to exhibits and other visitors by highlighting those visitors who share their thoughts; encouraging visitors' reflection on the social and liminal aspects of museum-going and their expectations of technology in museums; and doing this with transparency, aligning aesthetically pleasing elements of the design with the goals of connection and reflection. Deploying ArtLinks revealed that people have strong expectations of technology as an information appliance. Despite these expectations, people valued connections to other people, both for their own sake and as a way to support meaningful experience. We also found several of our design choices in the name of transparency led to unforeseen tradeoffs between the social and the liminal.

A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings.

Effective communication in project teams is important, but not often taught. We explore how feedback might improve teamwork in a controlled experiment where groups interact through chat rooms. Collaborators who receive high feedback ratings use different language than poor collaborators (e.g. more words, fewer assents, and less affect-laden language). Further, feedback affects language use. This suggests that a system could use linguistic analysis to automatically provide and visualize feedback to teach teamwork. To this end, we present GroupMeter, a system that applies principles discovered in the experiment to provide feedback both from peers and from automated linguistic analysis.

We describe the design and evaluation of K-net, a social matching system to help people learn 'who knows what' in an organization by matching people with skills with those who need them. Transactive memory theory predicts that K-net will improve individuals' awareness of 'who knows what'. This should lead to improved performance through sharing knowledge across group boundaries. We evaluate K-net in terms of these predictions in an experiment with 41 students in seven groups working on software engineering projects. Accurate recommendations improved awareness of 'who knows what' versus 'random' recommendations, but did not improve performance. Our results highlight issues related to the evaluation of systems for sharing knowledge across group boundaries.

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

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

A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.

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

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

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

Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings.

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

Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

NuggetMine is an intelligent groupware application that collaborates with a workgroup to increase information nugget sharing among the group. Information nuggets are small amounts of self-contained information, such as the URL of an interesting news article, a book title, or the time and location of a local art event. NuggetMine and the workgroup work together to build, maintain, and utilize a repository-or "mine"-of information nuggets. Group members submit nuggets to NuggetMine, which organizes and augments the submitted nuggets and provides a desktop interface to each group member. This interface makes it easy for group members to submit nuggets, view nuggets, and explore the mine. NuggetMine distributes the tasks necessary to share nuggets between it and the workgroup so as to best utilize the skills of each collaborator. In this paper, we describe the NuggetMine application and interface and present a pilot study of the application.

Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large pre-existing user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users. We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations. Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email.