Marketing campaigns using social media services aim to exploit social connections to propagate messages to potential customers. However, social activities often give rise to multiple network structures and some may be more effective in achieving the communication objectives than others. This led us to investigate a problem: given an observed sequence of messages and a social network that includes individuals involved in messaging, does the network structure 'explain' the observed propagation. To facilitate this investigation, we designed a method for encoding propagation events relative to the structure of a given network. The resulting transmission codes capture both the temporal and the structural characteristics of the propagation. We analyze the codes for maximal repeats and k-common sub strings to uncover dynamic network motifs within the propagation trace. By considering the dynamic motifs and the connected graph components, we can determine how the propagation events relate to the specific network. As a case study, we applied our method to rumor topics in Twitter and analyzed their propagation trails relative to the 'follower' network. The study demonstrates the computational feasibility of our approach and illustrates the use of dynamic motifs to reason about the impact of follower relationship rumor propagation in Twitter.

The MediaWiki platform supports popular socio-technical systems such as Wikipedia as well as thousands of other wikis. This software encodes and records a variety of relationships about the content, history, and editors of its articles such as hyperlinks between articles, discussions among editors, and editing histories. These relationships can be analyzed using standard techniques from social network analysis, however, extracting relational data from Wikipedia has traditionally required specialized knowledge of its API, information retrieval, network analysis, and data visualization that has inhibited scholarly analysis. We present a software library called the NodeXL MediaWiki Importer that extracts a variety of relationships from the MediaWiki API and integrates with the popular NodeXL network analysis and visualization software. This library allows users to query and extract a variety of multidimensional relationships from any MediaWiki installation with a publicly-accessible API. We present a case study examining the similarities and differences between different relationships for the Wikipedia articles about "Pope Francis" and "Social media." We conclude by discussing the implications this library has for both theoretical and methodological research as well as community management and outline future work to expand the capabilities of the library.

Over time, many people accumulate extensive email repositories that contain detailed information about their personal communication patterns and relationships. We present three visualizations that capture hierarchical, correlational, and temporal patterns present in user's email repositories. These patterns are difficult to discover using traditional interfaces and are valuable for navigation and reflection on social relationships and communication history. We interviewed users with diverse email habits and found that they were able to interpret these images and could find interesting features that were not evident to them through their standard email interfaces. The images also capture a wide range of variation in email practices. These results suggest that information visualizations of personal communications have value for end-users and analysts alike.

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 population's 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.

We describe a usage study of NetscanTech, a system that generates and publishes daily a range of social metrics across three dimensions: newsgroup, author, and thread, for a set of approximately 15,000 technical newsgroups in Usenet. We bring together three interlinked datasets: survey data, usage log data and social accounting data from Usenet participation, to triangulate the relationship between various user roles and differential usage of social metrics in NetscanTech. We found our most frequent users focused on information related to individual authors far more than any other information provided. In contrast, users that visited less frequently focused more on information related to newsgroups and viewing newsgroup metrics. Our results suggest features that designers and developers of online communities may wish to include in their interfaces to support the cultivation of different community roles.

In this paper, we describe a system used to link physical objects to online content implemented with commercially available pocket computers using bar code scanners, wireless networks, and web services. We describe sample applications built with the system for objects like books, music, packaged goods, and art works as well as a related web application that facilitates the creation of communities around objects scanned by the handhelds. Finally, we suggest several scenarios for uses of these kinds of devices and the possible sociological and commercial implications.

In this paper we describe an evaluation of behavioral descriptors generated from an analysis of a large collection of Usenet newsgroup messages. The metrics describe aspects of newsgroup authors' behavior over time; such information can aid in filtering, sorting, and recommending content from public discussion spaces like newsgroups. To assess the value of a variety of these behavioral descriptors, we compared 22 participants' subjective evaluations of authors whose messages they read to behavioral metrics describing the same authors. We found that many metrics, particularly the longevity and frequency of participation, the number of newsgroups to which authors contribute messages, and the amount they contribute to each thread, correlate highly with readers' subjective evaluations of the authors

An appropriately designed interface to persistent, threaded conversations could reinforce socially beneficial behavior by prominently featuring how frequently and to what degree each user exhibits such behaviors. Based on the data generated by the Netscan data-mining project [9], we have developed a set of tools for illustrating the structure of discussion threads like those found in Usenet newsgroups and the patterns of participation within the discussions. We describe the benefits and challenges of integrating these tools into a multi-faceted dashboard for navigating and reading discussions in social cyberspaces like Usenet and related interaction media. Visualizations of the structure of online discussions have applications for research into the sociology of online groups as well as possible interface designs for their members.

This paper provides a unique quantitative analysis of the social dynamics of three chat rooms in the Microsoft V-Chat graphical chat system. Survey and behavioral data were used to study user experience and activity. 150 V-Chat participants completed a web-based survey, and data logs were collected from three V-Chat rooms over the course of 119 days. This data illustrates the usage patterns of graphical chat systems, and highlights the ways physical proxemics are translated into social interactions in online environments. V-Chat participants actively used gestures, avatars, and movement as part of their social interactions. Analyses of clustering patterns and movement data show that avatars were used to provide nonverbal cues similar to those found in face-to-face interactions. However, use of some graphical features, in particular gestures, declined as users became more experienced with the system. These findings have implications for the design and study of online interactive environments.

We describe some common problems experienced by users of computer-based text chat, and show how many of these problems relate to the loss of timing-specific information. We suggest that thinking of chat as a real-time streaming media data type, with status and channel indicators, might solve some of these problems. We then present a number of alternative chat interfaces along with results from user studies comparing and contrasting them both with each other and with the standard chat interface. These studies show some potential, but indicate that more work needs to be done.