Eric Gleave

Analyzing (social media) networks with NodeXL

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.

Revisiting Whittaker & Sidner's "email overload" ten years later

Ten years ago, Whittaker and Sidner [8] published research on email overload, coining a term that would drive a research area that continues today. We examine a sample of 600 mailboxes collected at a high-tech company to compare how users organize their email now to 1996. While inboxes are roughly the same size as in 1996, our populations email archives have grown tenfold. We see little evidence of distinct strategies for handling email; most of our users fall into a middle ground. There remains a need for future innovations to help people manage growing archives of email and large inboxes.

Discussion catalysts in online political discussions: Content importers and conversation starters

This study addresses 3 research questions in the context of online political discussions: What is the distribution of successful topic starting practices, what characterizes the content of large thread-starting messages, and what is the source of that content? A 6-month analysis of almost 40,000 authors in 20 political Usenet newsgroups identified authors who received a disproportionate number of replies. We labeled these authors ‘‘discussion catalysts.’’ Content analysis revealed that 95 percent of discussion catalysts’ messages contained content imported from elsewhere on the web, about 2/3 from traditional news organizations. We conclude that the flow of information from the content creators to the readers and writers continues to be mediated by a few individuals who act as filters and amplifiers.

Whither the Experts? Social Affordances and the Cultivation of Experts in Community Q&A Systems

Community based Question and Answer systems have been promoted as web 2.0 solutions to the problem of finding expert knowledge. This promise depends on systems’ capacity to attract and sustain experts capable of offering high quality, factual answers. Content analysis of dedicated contributors’ messages in the Live QnA system found: (1) few contributors who focused on providing technical answers (2) a preponderance of attention paid to opinion and discussion, especially in non-technical threads. This paucity of experts raises an important general question: how do the social affordances of a site alter the ecology of roles found there? Using insights from recent research in online community, we generate a series of expectations about how social affordances are likely to alter the role ecology of online systems.

Trust in Uzbekistan

Although trust is a lively area of research, it is rarely investigated in countries outside of commonly available cross-national public-opinion datasets. In an effort to fill this empirical void and to draw conclusions concerning the general determinants of trust, the current article employs detailed survey data from a frequently overlooked Central Asian country, Uzbekistan, to test the relationship between particularized trust and demographic traits previously identified as influential. While a number of Uzbek demographic characteristics coincide with previously identified determinants of trust, age and education yield negative effects not previously found. Interestingly, individual-level demographic variables become insignificant when controlling for regional, religious, and linguistic variation. We conclude with a discussion of the theoretical implications.

Power-use in cooperative competition: A power-dependence model and an empirical test of network structure and geographic mobility

Although the social exchange paradigm has produced a vibrant research program, the theoretical tradition is rarely used to model the structure of social networks outside of experiments and simulations. To address this limitation, we derive power-dependence predictions about network structure and geographic mobility-the outcomes of power-use-and test these predictions using complete data on competition networks and travel schedules among amateur sports teams. Poisson regression and exponential random graph models provide strong support for our predictions. The findings illustrate exchange dynamics in which status resources desired by teams, coupled with the availability of geographically proximal alternatives, create power and dependence that dictate where and with whom teams compete. Although evidence supports Georg Simmels classic proposition that networks form on the basis of values and propinquity, we show that this complex dynamic is conditional on power and dependence. We conclude by discussing implications and directions for future research.

Reflections and Reactions to Social Accounting Meta-Data

Online systems are becoming increasingly social environments in which people share advice and experiences in threaded discussions, photos, videos and other files in systems like Flickr and You Tube, and display details of their social lives through a host of social networking sites. Yet even as these settings provide rich content, that content does not automatically provide us with social cues that can reveal what an interaction might mean, who we are interacting with, or the nature of their underlying character. As more of social life is embedded in these systems, we come to want and need systems for expressing identity and building reputations that can help us resolve some of this uncertainty. Because interaction in these settings leaves traces, however, we can look at histories and patterns of actions from hundreds of interactions. These types of accumulated reputations can reveal a great deal.