This study integrates network and content analyses to examine exposure to cross-ideological

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.

As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.

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Abstract

Abstract:The concept of "social role" has long been used in social science describe the intersection of behavioral, meaningful, and structural attributes that emerge regularly in particular settings and institutions. We use structural signature methods to identify key roles in a large distributed collaboration system (Wikipedia) by examining the distribution of edits across types of pages and the structure of relationships between editors. We distinguish between technical editors, substantive experts, vandal fighters, and social networkers and demonstrate key ways that their patterns of interaction and contribution differ. We conclude by considering how differential entry into and retention in particular roles may affect the operation of the large social system. IntroductionIt is difficult to gauge the social significance of Wikipedia, other than to say that, like Google, Wikipedia has forever changed how we use, find and think about information. Both pundits (like Stephen Colbert) and researchers (Stvilia et al. 2005, Giles 2005) have been pre-occupied with the question of whether the product of Wikipedia is of sufficient quality, or whether its pages constitute legitimate references (Read 2006). Others argue that the project will never achieve a sufficient level of quality relying on non-expert volunteers of unknown identity (Chesney). Instead of prognosticating about the potential of the Wikipedia project we contend that scholars should be more focused on understanding how Wikipedia has achieved the success that it has: a "pretty good" resource for a basic understanding of most any topic. How has the "pretty good" and incredibly extensive resource been achieved? And how has this been possible given the absence of the resources and controls of conventional firms and bureaucracies?Through the course of our research we have come to suspect that the success of Wikipedia has stemmed from two key sources: (1) infrastructural features that help people find their roles in the organization (2) technical innovations that allow substantial economies of scale in the performance of many of those roles. This paper concentrates on the former and is about finding roles in a double sense: to what extent can we identify the roles people play in Wikipedia by measuring general behavioral and structural features of their participation? We address this technical challenge by using a range of qualitative and quantitative methods to find signatures of social roles in Wikipedia (cite cite). The second sense asks: how do people find their roles in Wikipedia? We address this question quantitatively by comparing general patterns in editing across two different samples of Wikipedians, a cohort that first edited in January of 2005 and a sample of dedicated Wikipedians whose edits spanned a period of greater than one year. These samples allow us to compare how patterns in participation vary between cross section of new participants and those who stick around, and become the long term participants in the social system. Finding Social Role...

Chat programs and instant messaging services are increasingly popular among Internet users. However, basic issues with the interfaces and data structures of most forms of chat limit their utility for use in formal interactions (like group meetings) and decision-making tasks. In this paper, we discuss Threaded Text Chat, a program designed to address some of the deficiencies of current chat programs. Standard forms of chat introduce ambiguity into interaction in a number of ways, most profoundly by rupturing connections between turns and replies. Threaded Chat presents a solution to this problem by supporting the basic turn-taking structure of human conversation. While the solution introduces interface design challenges of its own, usability studies show that users' patterns of interaction in Threaded Chat are equally effective, but different (and possibly more efficient) than standard chat programs.

Usenet is a complex socio‐technical phenomenon, containing vast quantities of information. The sheer scope and complexity make it a challenge to understand the many dimensions across which people and communication are interlinked. In this work, we present visualizations of several aspects and scales of Usenet that combine to highlight the range of variation found in newsgroups. We examine variations within hierarchies, newsgroups, authors, and social networks. We find a remarkable diversity, with clear variations that mark starting points for mapping the broad sweep of behavior found in this and other social cyberspaces. Our findings provide the basis for initial recommendations for those cultivating, managing, contributing, or consuming collectively constructed conversational content.