This paper presents a study of a messaging interface prototype for online dating intended to improve women's face-to-face meeting decisions, and therefore the capacity to manage the gendered risks involved with such meetings. The interface prompts users with discussion topics that are potentially more valuable for user evaluation than the impression management-motivated topics often chosen by men. These topics come in the form of first-date scenarios that messaging partners either agree or disagree on. Through a mixed-methods study utilizing speed dating events, daters used the interface to interact before meeting face-to-face. Results indicate that women's face-to-face meeting decisions improve when the interface prompts them to discuss scenarios involving agreement of opinion. Men's decisions are worsened by the same interface variant, potentially due to the displayed agreement being misinterpreted as a signal of compatibility. The study ultimately stresses that designs intended for women, and at-risk groups more broadly, must also be assessed with other user demographics-namely those that pose a risk-to identify unforeseen implications.

Mobile social matching systems aim to bring people together in the physical world by recommending people nearby to each other. Going beyond simple similarity and proximity matching mechanisms, we explore a proposed framework of relational, social and personal context as predictors of match opportunities to map out the design space of opportunistic social matching systems. We contribute insights gained from a study combining Experience Sampling Method (ESM) with 85 students of a U.S. university and interviews with 15 of these participants. A generalized linear mixed model analysis (n=1704) showed that personal context (mood and busyness) as well as sociability of others nearby are the strongest predictors of contextual match interest. Participant interviews suggest operationalizing relational context using social network rarity and discoverable rarity, and incorporating skill level and learning/teaching needs for activity partnering. Based on these findings we propose passive context-awareness for opportunistic social matching.

Despite the popularity of online dating systems, prior work indicates that online daters struggle to present and evaluate traits germane to attraction. However, the research community is largely unaware of successful online dating system-use strategies and how the knowledge of these strategies could inform system design. In this paper we begin to address this gap in knowledge with an interview study of online dating coaches who consider their advocated system-use strategies successful based on personal use and feedback from clients who implemented their strategies. Their advocated strategies entail keeping online evaluation intentionally minimal and persuading users to meet in-person as quickly as possible because current system designs stifle reliable evaluation of attraction-relevant traits online, notably personality. We discuss the implications of insights gathered from these strategies for online dating system design using theories relevant to romantic attraction.

Social matching systems recommend people to people. In an ideal world, such systems could be context-aware, in that they would introduce users to each other in situations where they are mutually interested, available and open to meeting (i.e., facilitate a valuable encounter). Unfortunately, today's systems primarily match individuals based on simple similarity and proximity metrics. This paper explores how contextual information available on today's mobile phones could be used to identify opportunities for people to make valuable new connections. Three types of context that are relevant for this work are: relational, social and personal. We present insights gained from several iterations of semi-structured interviewing (N=58) exploring these three types of contexts and propose novel context-aware social matching concepts such as: sociability of others as an indicator of opportune social context; activity involvement as an indicator of opportune personal context; and contextual rarity as an indicator of opportune relational context.

Online dating systems are the most common way people meet their marriage partners online. Nevertheless, online daters struggle to evaluate personality traits of potential partners using profile pages and private messaging in these systems. Meanwhile, Multiplayer Online Games (MOGs) have emerged as a popular way young people find romantic partners for relationships in the physical world. We conducted two interview studies -- one concerning evaluation behavior in online dating systems (n=41) and the other concerning collaborative activities in MOGs (n=35). Insights from these studies reveal the weaknesses in evaluation tools native to online dating and suggest that collaborative activities could potentially address evaluation challenges in online dating. The paper concludes with a discussion of a series of design concepts for online dating in order to improve users' abilities to evaluate their potential romantic partners for in-person meetings.

Mobile social matching systems have the potential to transform the way we make new social ties, but only if we are able to overcome the many challenges that exist as to how systems can utilize contextual data to recommend interesting and relevant people to users and facilitate valuable encounters between strangers. This article outlines how context and mobility influence people's motivations to meet new people and presents innovative design concepts for mediating mobile encounters through context-aware social matching systems. Findings from two studies are presented. The first, a survey study (n = 117) explored the concept of contextual rarity of shared user attributes as a measure to improve desirability in mobile social matches. The second, an interview study (n = 58) explored people's motivations to meet others in various contexts. From these studies we derived a set of novel context-aware social matching concepts, including contextual sociability and familiarity as an indicator of opportune social context; contextual engagement as an indicator of opportune personal context; and contextual rarity, oddity, and activity partnering as an indicator of opportune relational context. The findings of these studies establish the importance of different contextual factors and frame the design space of context-aware social matching systems.

This paper explores how the adoption of mobile and social computing technologies has impacted upon the way in which we coordinate social group-activities. We present a diary study of 36 individuals that provides an overview of how group coordination is currently performed as well as the challenges people face. Our findings highlight that people primarily use open-channel communication tools (e.g., text messaging, phone calls, email) to coordinate because the alternatives are seen as either disrupting or curbing to the natural conversational processes. Yet the use of open-channel tools often results in conversational overload and a significant disparity of work between coordinating individuals. This in turn often leads to a sense of frustration and confusion about coordination details. We discuss how the findings argue for a significant shift in our thinking about the design of coordination support systems.

Despite the importance of individuals coming together for social group-activities (e.g. pick-up volleyball, chess clubs), the process by which such groups coalesce is poorly understood. Existing theories focus on adoption and contribution rates, group types, and the formation of group norms, as opposed to the processes involved in initial group coalescence. We address this gap in the literature through an interview study examining: 1) how well people's needs for social group activity engagement are being met; 2) the challenges they face in finding and participating in, and; 3) leading interest-based group activities. Our findings highlight how people-s needs are not being addressed by current technologies. In particular, they place a heavy burden on individuals to step forward into leadership positions where the return they will receive for their efforts is often unknown, or extremely limited. We discuss the implications of our findings for the design of interest-based group coalescing technology.

Online dating systems are now widely used to search for romance and yet there is little research on how people use these systems to manage their impressions with potential romantic partners. To address this issue we conducted an interview study of 41 online dating users, revealing that-contrary to prior work-online daters largely do not want to intentionally deceive their online dating partners because they think such lies would quickly be discovered face-to-face. Nevertheless, bad first dates were a norm rather than an exception for this study's participants. In this paper we present various frustrations online daters associate with conveying and forming impressions of potential romantic partners before meeting face-to-face. We discuss the implications of these findings for the design of online dating systems.

Social computing applications are transforming the way we make new social ties, work, learn and play, thus becoming an essential part our social fabric. As a result, people and systems routinely make inferences about people's personal information based on their disclosed personal information. Despite the significance of this phenomenon the opportunity to make social inferences about users and how this process can be managed is poorly understood. In this paper we 1) outline why social inferences are important to study in the context of social computing applications, 2) how we can model, understand and predict social inference opportunities 3) highlight the need for social inference management systems, and 4) discuss the design space and associated research challenges. Collectively, this paper provides the first systematic overview for social inference research in the area of social computing.

Current cell phone designs are limited by the information a caller can provide to the receiver at the time of a call. As a result callers are handicapped in effectively negotiating interaction commitment from the receiver, and perhaps more importantly, receivers are unable to make informed call handling decisions. To examine the nature of this information gap we 1) developed Telling Calls, a mobile phone application which allows users to provide and receive information such as what the call is about and the circumstances of the caller under which it is being made, and 2) conducted a qualitative field study (36 users) and a quantitative field study (30 users) of Telling Calls use. Together these studies provide insights on how additional caller generated information shared at the time of call handling effectively improves the process of negotiating interaction commitment, and establishing common ground.

Call handling decisions are made based on explicit information such as caller ID and/or implicit knowledge inferred from previous interactions. Our previous work showed that people often answer calls to find out the reason behind the call. This suggests that the provision of explicit information regarding what a call is about or under what circumstances is it being made can be of value in call handling decisions. To explore this concept we developed Telling Calls a prototype application for cell phone users to provide and receive call related information. We present the design rationale and lessons learned from qualitative accounts of users' experience with the application in their daily life. Our findings confirm the utility of our design and suggest ways in which we can improve the design to support informed call handling decisions in mobile phones.

There is general consensus that critical mass at inception ensures the sustained success of online communities. However, no clear understanding of what constitutes such a 'critical mass ' exists and too few quantitative studies have been conducted into the relationship between initial online community interaction and its longer term success to draw any conclusions. In this paper we start to address this gap through a large-scale study of the relationship between IRC chat channel survival and initial chat channel community interactions. A sample 282 chat channel births was used for survival analysis which explored the relationship between the overall user activity in each channel at its inception and the channel's life expectancy. Significant relationships were observed between online community lifespan and critical mass measures: 1) message volume, 2) user population heterogeneity and 3) production functions. The results lend support to the Critical Mass Theory of collective action.

Social matching systems recommend people to other people. With the widespread adoption of smartphones, mobile social matching systems could potentially transform our social landscape. However, we have a limited understanding of what makes a good social match in the mobile context. We present a theoretical framework which outlines how a user's context and the rarity of different affinity measures in various contexts (match rarity) can be used to provide valuable social matches. We suggest that if a user attribute is very rare in a particular context, users will generally be more interested in an affinity match. We conducted a survey study to assess this framework with 117 respondents. We found that both context and match rarity significantly influence interest in a social match. These results validate the key aspects of the framework. We discuss the results in terms of implications for social matching system design.

In any situation where a set of personal attributes are revealed, there is a chance that revealed data can be linked back to its owner. Examples of such situations are publishing user profile micro-data or information about social ties, sharing profile information on social networking sites, or revealing personal information in computer-mediated communication (CMC). Measuring user anonymity is the first step to ensuring that the identity of the owner of revealed information cannot be inferred. Most current measures of anonymity ignore important factors such as the probabilistic nature of identity inference, the inferrer's outside knowledge, and the correlation between user attributes. Furthermore, in the social computing domain, variations in personal information and various levels of information exchange among users make the problem more complicated. We present an information-entropy-based realistic estimation of the user anonymity level to deal with these issues in social computing in an effort to help predict the identity inference risks. We then address implementation issues of online protection by proposing complexity reduction methods that take advantage of basic information entropy properties. Our analysis and delay estimation based on experimental data show that our methods are viable, effective, and efficient in facilitating privacy in social computing and synchronous CMCs.

A serious threat to user privacy in new mobile and Web2.0 applications stems from `social inferences'. These unwanted inferences are related to the users' identity, current location and other personal information. We have previously introduced `inference functions' to estimate the social inference risk based on information entropy. In this paper, after analyzing the problem and reviewing our risk estimation method, we create a decision tree to distinguish between high risk and normal situations. To evaluate our methodology, test and training datasets were collected during a large mobile-phone field study for a location-aware application. The classification tree employs our two inference functions, for the current and past situations, as internal nodes. Our results show that the achieved true classification rates are significantly better than approaches that employ other available features for the internal nodes of the trees. The results also suggest that common classification tools cannot accurately capture the information entropy for social applications. This is mostly due to the lack of enough training data for high-risk, low-entropy situations and outliers. Thus, we conclude that estimating the information entropy and the relevant inference risk using a pre-processor can yield a simpler and more accurate classification tree.

No abstract available.

Prior work has demonstrated that the impact of individual information-processing limits can be observed in dynamics of mass interaction in asynchronous collaborative systems (Usenet newsgroups and email lists). Here we present the first evidence of such impacts on synchronous social interaction environments through the analysis of an Internet Relay Chat network. We highlight how shared public discourse in chat channels appears to be limited to 40 posters in any 20 minute interval, even as the number of channel users increases well into the hundreds. We discuss our findings in terms of understanding the relationship between online community space types and the user interaction dynamics they support.

Review of the literature suggests seven fundamental privacy challenges in the domain of ubiquitous social computing. To date, most research in this area has focused on the features associated with the revelation of personal location data. However, a more holistic view of privacy concerns that acknowledges these seven risks is required if we are to deploy privacy respecting next generation social computing applications. We highlight the threat associated with user inferences made possible by knowledge of the context and use of social ties. We also describe work in progress to both understand user perceptions and build a privacy sensitive urban enclave social computing system. 1.

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

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

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

This paper provides a comprehensive review of empirical research into user contributions to computer-mediated discourse in public cyber-spaces, referred to here as virtual publics. This review is used to build a systems model of such discourse. The major components of the model are i) critical mass, ii) social loafing, and iii) the collective impact of individual cognitive constraints on the processing of group messages. By drawing these three components into a single model it becomes possible to describe the shape of a “user-contributions/user-population function” after controlling for context.