This study integrates network and content analyses to examine exposure to cross-ideological political views on Twitter. We mapped the Twitter networks of 10 controversial political topics, discovered clusters – subgroups of highly self-connected users – and coded messages and links in them for political orientation. We found that Twitter users are unlikely to be exposed to cross-ideological content from the clusters of users they followed, as these were usually politically homogeneous. Links pointed at grassroots web pages (e.g.: blogs) more frequently than traditional media websites. Liberal messages, however, were more likely to link to traditional media. Last, we found that more specific topics of controversy had both conservative and liberal clusters, while in broader topics, dominant clusters reflected conservative sentiment.
This two-volume encyclopedia provides a thorough introduction to the wide-ranging, fast-developing field of social networking, a much-needed resource at a time when new social networks or “communities” seem to spring up on the internet every day. Social networks, or groupings of individuals tied by one or more specific types of interests or interdependencies ranging from likes and dislikes, or disease transmission to the “old boy” network or overlapping circles of friends, have been in existence for longer than services such as Facebook or YouTube; analysis of these networks emphasizes the relationships within the network. The Encyclopedia of Social Networks offers comprehensive coverage of the theory and research within the social sciences that has sprung from the analysis of such groupings, with accompanying definitions, measures, and research.
Featuring approximately 350 signed entries, along with approximately 40 media clips, organized alphabetically and offering cross-references and suggestions for further readings, this encyclopedia opens with a thematic reader’s guide in the front that groups related entries by topics. A chronology offers the reader historical perspective on the study of social networks. This two-volume reference work is a must-have resource for libraries serving researchers interested in the various fields related to social networks, including sociology, social psychology and communication and media studies.
The paper is authored by: Howard T. Welser at Ohio University, Austin Lin at Cornell University and Microsoft, Dan Cosley, Fedor Dokshin, Gueorgi Kossinets and Geri Gay at Cornell University, and Marc Smith from Connected Action.
Abstract: This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential “structural signatures” that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month’s cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.
Our paper is about visualizing social media and it describes the visualization of the patterns of connections formed when people tweet about events like conferences and news stories.
EventGraphs are social media network diagrams constructed from content selected by its association with time-bounded events, such as conferences. Many conferences now communicate a common “hashtag” or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The paper also identifies the structural and conversational patterns to look for and highlight in EventGraphs and provides design ideas for their improvement.
Here is the data set: 20110109-NodeXL-Twitter-HICSS
Graph Metric Value
Graph Type Directed
Unique Edges 243
Edges With Duplicates 71
Total Edges 314
Connected Components 21
Single-Vertex Connected Components 18
Maximum Vertices in a Connected Component 69
Maximum Edges in a Connected Component 307
Maximum Geodesic Distance (Diameter) 8
Average Geodesic Distance 3.081693
Graph Density 0.032967033
From the UMD News desk:
“A new study by University of Maryland researchers finds a
growing use of Twitter among members of Congress, but that
the purpose and content of their messages fall short of
improving government transparency.
Jennifer Golbeck, assistant professor in the College of Information
Studies, Maryland’s iSchool, a doctoral student and an undergraduate
assistant analyzed more than 5,000 tweets sent by 69 members of
Congress in February. They found that House and Senate members
were using the social media platform mostly to promote themselves,
rather than engage in dialogue with constituents and the public at large.
“Members of Congress were not sharing much new information on
Twitter, and there were few posts that improve transparency,”
This weekend is the Social Computing 2009 conference in Vancouver, B.C. It is a gathering of many people doing research on social media useage. Many papers are about tagging systems, blogs, wikis, message boards, and social networking services.
Abstract: Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sensemaking for students of SNA.
Abstract: Broadening adoption of social media applications within the enterprise offers a new and valuable data source for insight into the social structure of organizations. Social media applications generate networks when employees use features to create “friends” or “contact” networks, reply to messages from other users, edit the same documents as others, or mention the same or similar topics. The resulting networks can be analyzed to reveal basic insights into an organization’s structure and dynamics. The creation and analysis of sample social media network datasets is described to illustrate types of enterprise networks and considerations for their analysis.
Abstract: 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.
The ASA attracts thousands of sociologists, a subsection of whom have a passion for the study of the Internet and its many forms of social impacts and uses. The Communications and Information Technology Section of the American Sociological Association (CITASA) is the group that gathers many forms of social science research on the creation and uses of information technology. This year’s meeting included two CITASA panels, round tables, a business meeting with awards, and a (windy!) boat ride through San Francisco Bay and beneath the Golden Gate Bridge.
The paper describes the roles of “discussion cataylsts” who populate political web boards (newsgroups) and start the threads that get people talking! It turns out that only a very few people in a community get to start many threads successfully. Discussion catalysts have a knack for sparking conversations: setting the agenda for the community at large. Discussion people have high “-in-degree”, they get replied to by lots of people, but low “out-degree”, they tend not to reply that much themselves. The people whoreply to discussion catalysts, in contrast, do reply to one another densely. These are the discussion people, a role that will be the focus of a subsequent paper!
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.
Previously, we published “Picturing Usenet” in the JCMC, a paper that features several images of information visualizations of threaded discussions and authors over time. That paper was based on an early work with Fernanda Viegas (now at IBM Research, Cambridge, then as an MIT graduate student in the MediaLab interning with me at Microsoft Research in Redmond, Washington).
[2009 – JCMC- Discussion Catalysts – Himelboim, Gleave and Smith]
I just read a new paper from Jennifer Preece and Ben Shneiderman that provides a nice framework for the ways people contribute at different rates to collective projects in general and social media on the Internet in particular.
Available at: http://aisel.aisnet.org/thci/vol1/iss1/5, the paper is published in a new journal, Association for Information Systems Transactions on Human-Computer Interaction, and is likely to be of interest to those in the social media and network analysis community. The main argument is that there are distinctive activities that people move through: initially as readers, then contributors (in small then larger ways), then collaborating with others to make larger contributions, and then to leadership (policy making, enforcement, coping with disruptions, mentoring novices, etc.). The figure (above) from the paper is modeled on Wikipedia where these activities have been studied extensively, but they argue that these activities can be found in many technology-mediated social media. The conversion rate from one activity to another is often as low as 1 percent (for example, there are half-a -billion readers of wikipedia, but just 1600 admins who are effectively the leaders), so the paper offers suggestions for improving the usability and sociability design to raise the conversion rate.