Research In Social Computing Research Articles

Many Destinations, Many Pathways: A Quantitative Analysis of Legitimate Peripheral Participation in Scratch

Although informal online learning communities have proliferated over the last two decades, a fundamental question remains: What are the users of these communities expected to learn? Guided by the work of Etienne Wenger on communities of practice, we identify three distinct types of learning goals common to online informal learning communities: the development of domain skills, the development of identity as a community member, and the development of community-specific values and practices. Given these goals, what is the best way to support learning? Drawing from previous research in social computing, we ask how different types of legitimate peripheral participation by newcomers-contribution to core tasks, engagement with practice proxies, social bonding, and feedback exchange-may be associated with these three learning goals. Using data from the Scratch online community, we conduct a quantitative analysis to explore these questions. Our study contributes both theoretical insights and empirical evidence on how different types of learning occur in informal online environments.

Identifying Competition and Mutualism between Online Groups

Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology, prior research in social computing and human-computer interaction has studied related groups by correlating group size with degrees of overlap in content and membership, but has produced puzzling results: overlap is associated with competition in some contexts but with mutual-ism in others. We suggest that this inconsistency results from aggregating intergroup relationships into an overall environmental effect that obscures the diversity of competition and mutualism among related groups. Drawing on the framework of community ecology, we introduce a time-series method for inferring competition and mutualism. We then use this frame-work to inform a large-scale analysis of clusters of subreddits that all have high user overlap. We find that mutualism is more common than competition.

Managing Organizational Culture in Online Group Mergers

Research in social computing has typically conceptualized community growth as a process through which a group welcomes newcomers individually. However, online communities also grow through formal and informal mergers, where groups of newcomers with shared experiences join in batches. To understand this process, we conducted a six month, comparative ethnography of two mergers of World of Warcraft raid guilds. While one merger led to a single, thriving community, the other led to the dissolution of both pre-merger groups. Analysis of our ethnographic data suggests that differences in managing organizational culture (a concept drawn from organization studies) led the successful and failed cases to diverge. The study contributes to our understanding of why some attempts to integrate members of different communities are more successful than others. We outline several ways that community leaders, researchers, and designers can effectively take organizational culture into account.

Ethics Regulation in Social Computing Research: Examining the Role of Institutional Review Boards.

The parallel rise of pervasive data collection platforms and computational methods for collecting, analyzing, and drawing inferences from large quantities of user data has advanced social computing research, investigating digital traces to understand mediated behaviors of individuals, groups, and societies. At the same time, methods employed to access these data have raised questions about ethical research practices. This article provides insights into U.S. institutional review boards' (IRBs) attitudes and practices regulating social computing research. Through descriptive and inferential analysis of survey data from staff at 59 IRBs at research universities, we examine how IRBs evaluate the growing variety of studies using pervasive digital data. Findings unpack the difficulties IRB staff face evaluating increasingly technical research proposals while highlighting the belief in their ability to surmount these difficulties. They also indicate a lack of consensus among IRB staff about what should be reviewed and a willingness to work closely with researchers.

Opinion: Learning as we go: lessons from the publication of Facebook's social-computing research.

In the aftermath of the publication of the “emotional contagion” study conducted by Facebook researchers working with Cornell University scholars (1, 2), many observers weighed in about everything from the acceptability of undertaking the research at all, to how it was conducted, the rules and regulations that applied to it, and even the advisability of publishing the article. The views were disparate and conflicting (3⇓⇓–6). Our goal in this Opinion is not to try to settle these debates, but rather to attempt to draw some general lessons and offer recommendations from an ethics perspective as large-scale social-computing research moves forward. Our motivation is not to defend or chastise the research and technology communities or those responsible for ethics oversight of research. Instead, we wish to suggest that the development and application of an appropriate ethical framework and some form of ethics oversight is a moral imperative that is also in the interest of all. First, such oversight acts as a crucial signal of rules and accountability, which can increase overall trust and, in turn, the willingness to support and participate in research. Second, oversight leads to more credible research that others can build upon and funders and investors will support. Third, consistent approaches ease and encourage the trust and legitimacy needed for partnership and collaborations, which is the basis of 21st century science of all kinds.

Characterizing the Evolution of Social Computing Research

With Web 2.0 advances, social computing has become an emerging research field in the past decade. This article analyzes the characteristics of social computing research from both static and dynamic perspectives. First, the authors present the overlapping relationships of content represented by keywords as of 2011. Next, they show the dynamics of social computing research through analyzing keyword trends and topological evolution of co-word networks. The article characterizes the key features and the evolution of social computing from a quantitative perspective.

On the Interpretation of Digital Trace Data in Communication and Social Computing Research

The widespread availability of analytical tools for Big Data offers enormous opportunities and challenges for communication researchers. In contrast to user-generated texts, digital trace data (evidence of online user activities such as hyperlinks and retweets) represent a new methodological frontier for the field. However, interpretive strategies remain scattered and ad hoc with few best practices to guide them. To help remedy this situation, this article reviews recent scholarship in both communication and social computing research that has incorporated three common types of trace data: hyperlinks, Twitter followers, and retweets. It finds that while researchers in both fields have interpreted each trace in a variety of ways, they have largely declined to explain the validity of their interpretations.

On social computing research collaboration patterns: a social network perspective

The field of social computing emerged more than ten years ago. During the last decade, researchers from a variety of disciplines have been closely collaborating to boost the growth of social computing research. This paper aims at identifying key researchers and institutions, and examining the collaboration patterns in the field. We employ co-authorship network analysis at different levels to study the bibliographic information of 6 543 publications in social computing from 1998 to 2011. This paper gives a snapshot of the current research in social computing and can provide an initial guidance to new researchers in social computing.

Middleware for social computing: a roadmap

AbstractSocial computing broadly refers to supporting social behaviours using computational systems. In the last decade, the advent of Web 2.0 and its social networking services, wikis, blogs, and social bookmarking has revolutionised social computing, creating new online contexts within which people interact socially (socialnetworking). With the pervasiveness of mobile devices and embedded sensors, we stand at the brink of another major revolution, where the boundary between online and offline social behaviours blurs, providing opportunities for (re)defining social conventions and contexts once again. But opportunities come with challenges: can middleware foster the engineering of social software? We identify threesocietalgrand challenges that are likely to drive future research in social computing and elaborate on how the middleware community can help address them.

Guest Editors' Introduction: Social Computing

Broadly stated, social computing takes a computational approach to the study and modeling of social interactions and communications. It also encompasses the development of technologies supporting these interactions. In recent years, we've seen social computing impact numerous information and communications technology (ICT) fields. It's attracted significant interest from not only researchers in the computing and social sciences but also software and online game vendors, Web entrepreneurs, political analysts, and digital-government practitioners, among others. This special issue samples the state of the art social-computing research from several perspectives: the overall paradigm of social-computing research; technological support for social-computing applications; cognitive modeling and architecture of agents and agent societies; and social-computing applications in areas such as terrorist network analysis, competitive business strategies, and agent behavior in financial markets. This article is part of a special issue on social computing.

Guest Editors' Introduction: Social Computing

Broadly stated, social computing takes a computational approach to the study and modeling of social interactions and communications. It also encompasses the development of technologies supporting these interactions. In recent years, we've seen social computing impact numerous information and communications technology (ICT) fields. It's attracted significant interest from not only researchers in the computing and social sciences but also software and online game vendors, Web entrepreneurs, political analysts, and digital-government practitioners, among others. This special issue samples the state of the art social-computing research from several perspectives: the overall paradigm of social-computing research; technological support for social-computing applications; cognitive modeling and architecture of agents and agent societies; and social-computing applications in areas such as terrorist network analysis, competitive business strategies, and agent behavior in financial markets. This article is part of a special issue on social computing.