Successful online communities accumulate large amounts of long-term content. However there has been little quantitative, theoretically-motivated exploration of how communities organize such content, nor which community members take responsibility for active organization. We examine one aspect of long-term content organization through link behavior, also exploring role differences between enterprise community leaders and members in the context of life-cycle community models. We first classify how content is linked within posts, identifying usage patterns that organize information within and outside communities. We next present an exploratory quantitative analysis of 2,010 communities including 428,476 posts and 1,246,570 links. We show paradoxically that although mature communities accumulate substantial content, organizing that content using links decreases over time. Further analyses suggest that this arises from a recency bias, with communities being focused on current content. Our results also challenge descriptive lifecycle community models, which propose that regular community members adopt greater responsibility over time. We explore explanations for our findings and implications including new tools that encourage responsibility for active organization, as well as methods for members to revisit critical content.

Research on small team collaboration repeatedly shows that 'distance matters'. More recent work has refined this concept of distance to develop geographic dispersion measures to explain the negative effects that team configuration has on productivity and interaction. Dispersion measures explain why teams distributed across multiple time zones, or across multiple sites, have more coordination difficulties than collocated teams with a single remote member. Although larger online communities are increasingly used in enterprises, few studies have examined the effects of dispersion on community behavior. We studied 1206 online enterprise communities (OECs), each using a set of collaborative tools. We present new data showing counter-intuitively that OEC dispersion does not affect content generation or contribution inequality, even when restricting community size to those that most resemble small teams (with 3-12 members). We found that previously documented negative effects of geographic dispersion seem to be reduced in enterprise communities regardless of size. Our results provide additional support to prior case studies suggesting that online communities can mitigate geographic dispersion by providing resources that support coordination and resource sharing.

Recent research has developed analytics that threaten online self-presentation and privacy by automatically generating profiles of individuals' most personal traits-their personality, values, motivations, and so on. But we know little about people's reactions to personal traits profiles of themselves, or what influences their decisions to share such profiles. We present an early qualitative study of people's reactions to a working hyper-personal analytics system. The system lets them see their personality and values profile derived from their own social media text. Our results reveal a paradox. Participants found their personal traits profiles creepily accurate and did not like sharing them in many situations. However, they felt pressured by the social risks of not sharing and showed signs of learned helplessness, leading them to share despite their misgivings. Further, they felt unqualified to significantly modify their profile contents due to a surprising trust in the "expert" algorithm. We explore design implications for hyper-personal analytics systems that consider the needs and preferences of the people being profiled, suggesting ways to enhance the control they feel and the benefits they reap.

Online communities are successful only if they achieve their goals, but there has been little direct study of goals. We analyze novel data characterizing the goals of enterprise online communities, assessing the importance of goals for leaders, how goals influence member perceptions of community value, and how goals relate to success measures proposed in the literature. We find that most communities have multiple goals and common goals are learning, reuse of resources, collaboration, networking, influencing change, and innovation. Leaders and members agree that all of these goals are important, but their perceptions of success on goals do not align with each other, or with commonly used behavioral success measures. We conclude that simple behavioral measures and leader perceptions are not good success metrics, and propose alternatives based on specific goals members and leaders judge most important.

Most studies of social tools examine usage of each tool in isolation. Instead, we explore how online communities (a) combine multiple social tools, and (b) use social tools together with external tools. Based on interviews with community leaders and quantitative analysis of 128 online community spaces, we explored the combined use of six social software tools--wikis, blogs, forums, social bookmarks, social file repositories, and task-management tools. We contribute a detailed characterization of how enterprise online communities combine multiple social tools, adding to our understanding of community behaviors: Communities combine social tools to curate and organize complex information spaces. When combined, each tool is used for limited "core" functions; thus 'social' features are not always leveraged for every tool. Leaders and members divide labor by tool boundaries. Our results suggest that an important overlooked aspect of social media concerns how different tools can be effectively combined. While most prior work on communities emphasizes end user content, we identify additional important design activities where community participants curate and organize pre-existing content from multiple tools to serve their community needs.

This paper presents a system, System U, which automatically derives people's personality traits from social media and recommends people for different tasks. The system leverages linguistic signals appearing in a person's social media activities to compute the personality portraits including Big Five personality, fundamental needs and basic human values. This system and technology can be used in a wide variety of personalized applications, such as recommending people to answer questions.

Online communities are increasingly being deployed in enterprises to increase productivity and share expertise. Community leaders are critical for fostering successful communities, but existing technologies rarely support leaders directly, both because of a lack of clear data about leader needs, and because existing tools are member- rather than leader-centric. We present the evidence-based design and evaluation of a novel tool for community leaders, Community Insights (CI). CI provides actionable analytics that help community leaders foster healthy communities, providing value to both members and the organization. We describe empirical and system contributions derived from a long-term deployment of CI to leaders of 470 communities over 10 months. Empirical contributions include new data showing: (a) which metrics are most useful for leaders to assess community health, (b) the need for and how to design actionable metrics, (c) the need for and how to design contextualized analytics to support sensemaking about community data. These findings motivate a novel community system that provides leaders with useful, actionable and contextualized analytics.

An online community consists of a group of users who share a common interest, background, or experience and their collective goal is to contribute towards the welfare of the community members. Question answering is an important feature that enables community members to exchange knowledge within the community boundary. The overwhelming number of communities necessitates the need for a good question routing strategy so that new questions gets routed to the appropriately focused community and thus get resolved. In this paper, we consider the novel problem of routing questions to the right community and propose a framework to select the right set of communities for a question. We begin by using several prior proposed features for users and add some additional features, namely language attributes and inclination to respond, for community modeling. Then we introduce two k nearest neighbor based aggregation algorithms for computing community scores. We show how these scores can be combined to recommend communities and test the effectiveness of the recommendations over a large real world dataset.

REACH is an intelligent, people-finding system that helps users to find someone in their social directory, especially those whom they do not fully remember or barely know. It analyzes a user's communication and social networking data to automatically extract all the contacts and derive multiple facets to characterize each contact in relation to the user. It then employs a personalized, faceted search to retrieve and present a ranked list of matched contacts based on their properties. A preliminary evaluation shows the effectiveness of our approach.

A keyboard design, once adopted, tends to have a longlasting and worldwide impact on daily user experience. There is a substantial body of research on touch-screen stylus keyboard optimization. Most of it has focused on English only. Applying rigorous mathematical optimization methods and addressing diacritic character design issues, this article expands this body of work to French, Spanish, German, and Chinese. More important and counter to the intuition that optimization by nature is necessarily specific to each language, this article demonstrates that it is possible to find common layouts that are highly optimized across multiple languages for stylus (or single finger) typing. We first obtained a layout that is highly optimized for both English and French input. We then obtained a layout that is optimized for English, French, Spanish, German, and Chinese pinyin simultaneously, reducing its stylus travel distance to about half of QWERTY's for all of the five languages. In comparison to QWERTY's 3.31, 3....

It has been well understood that optimized soft keyboard layouts improve motor movement efficiency over the standard Qwerty layouts, but have the drawback of long initial visual search time for novice users. To ease the initial searching time on optimized soft keyboards, we explored "Quasi-Qwerty optimization" so that the resulting layouts are close to Qwerty. Our results show that a middle ground between the optimized but new, and the familiar (Qwerty) but inefficient does exist. We show that by allowing letters to move at most one step (key) away from their original positions on Qwerty in an optimization process, one can achieve about half of what free optimization could gain in movement efficiency. An experiment shows that due to users' familiarity with Qwerty, a layout with quasi Qwerty optimization could significantly reduce novice user's visual search time to a level between those of Qwerty and a freely optimized layout. The results in this work provide designers with a new quantitative understanding of the soft keyboard design space.

It is generally recognized that today's frontier of HCI research lies beyond the traditional desktop computers whose GUI interfaces were built on the foundation of display-pointing device-full keyboard.Many interface challenges arise without such a physical UI foundation.Text writingranging from entering URLs and search queries, filling forms, typing commands, to taking notes and writing emails and chat messages-is one of the hard problems awaiting for solutions in off-desktop computing.This paper summarizes and synthesizes a research program on this topic at the IBM Almaden Research Center.It analyzes various dimensions that constitute a good text input interface; briefly reviews related literature; discusses the evaluation methodology issues of text input; presents the major ideas and results of two systems, ATOMIK and SHARK; and points out current and future directions in the area from our current vantage point.

In this paper, we describe the use of eye gaze tracking and trajectory analysis in the testing of the performance of input devices for cursor control in Graphical User Interfaces (GUIs). By closely studying the behavior of test subjects performing pointing tasks, we can gain a more detailed understanding of the device design factors that may influence the overall performance with these devices. Our Results show them are many patterns of hand eye coordination at the computer interface which differ from patterns found in direct hand pointing at physical targets (Byrne, Anderson, Douglass, & Matessa, 1999).

Text entry user interfaces have been a bottleneck of nontraditional computing devices.One of the promising methods is the virtual keyboard on touch screens.Various layouts have been manually designed to replace the dominant QWERTY layout.This paper presents two computerized quantitative design techniques to search for the optimal virtual keyboard.The first technique simulated the dynamics of a keyboard with "digraph springs" between keys, which produced a "Hooke's" keyboard with 41.6 wpm performance.The second technique used a Metropolis random walk algorithm guided by a "Fitts energy" objective function, which produced a "Metropolis" keyboard with 43.1 wpm performance.The paper also models and evaluates the performance of four existing keyboard layouts.We corrected erroneous estimates in the literature and predicted the performance of QWERTY, CHUBON, FITALY, OPTI to be in the neighborhood of 30, 33, 36 and 38 wpm respectively.Our best design was 40% faster than QWERTY and 10% faster than OPTI, illustrating the advantage of quantitative user interface design techniques based on models of human performance over traditional trial and error designs guided by heuristics.