We propose an automated and unsupervised methodology for a novel summarization of group behavior based on content preference. We show that graph theoretical community evolution (based on similarity of user preference for content) is effective in indexing these dynamics. Combined with text analysis that targets automatically-identified representative content for each community, our method produces a novel multi-layered representation of evolving group behavior. We demonstrate this methodology in the context of political discourse on a social news site with data that spans more than four years and find coexisting political leanings over extended periods and a disruptive external event that lead to a significant reorganization of existing patterns. Finally, where there exists no ground truth, we propose a new evaluation approach by using entropy measures as evidence of coherence along the evolution path of these groups. This methodology is valuable to designers and managers of online forums in need of granular analytics of user activity, as well as to researchers in social and political sciences who wish to extend their inquiries to large-scale data available on the web.
In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 "A-list" blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern. Is the Internet democratizing American politics? Do political Web sites and blogs mobilize inactive citizens and make the public sphere more inclusive? The Myth of Digital Democracy reveals that, contrary to popular belief, the Internet has done little to broaden political discourse but in fact empowers a small set of elites--some new, but most familiar. Matthew Hindman argues that, though hundreds of thousands of Americans blog about politics, blogs receive only a miniscule portion of Web traffic, and most blog readership goes to a handful of mainstream, highly educated professionals. He shows how, despite the wealth of independent Web sites, online news audiences are concentrated on the top twenty outlets, and online organizing and fund-raising are dominated by a few powerful interest groups. Hindman tracks nearly three million Web pages, analyzing how their links are structured, how citizens search for political content, and how leading search engines like Google and Yahoo! funnel traffic to popular outlets. He finds that while the Internet has increased some forms of political participation and transformed the way interest groups and candidates organize, mobilize, and raise funds, elites still strongly shape how political material on the Web is presented and accessed. The Myth of Digital Democracy. debunks popular notions about political discourse in the digital age, revealing how the Internet has neither diminished the audience share of corporate media nor given greater voice to ordinary citizens. A general nonlinear logit model is used to analyze political choice data. The model assumes probabilistic voting based on a spatial utility function. The parameters of the utility function and the spatial coordinates of the choices and the choosers can all be estimated on the basis of observed choices. Ordinary Guttman scaling is a degenerate case of this model. Estimation of the model is implemented in the NOMINATE program for one dimensional analysis of two alternative choices with no nonvoting. The robustness and face validity of the program outputs are evaluated on the basis of roll call voting data for the U.S. House and Senate. Information technology can link geographically separated people and help them locate interesting or useful resources. These attributes have the potential to bridge gaps and unite communities. Paradoxically, they also have the potential to fragment interaction and divide groups. Advances in technology can make it easier for people to spend more time on special interests and to screen out unwanted contact. Geographic boundaries can thus be supplanted by boundaries on other dimensions. This paper formally defines a precise set of measures of information integration and develops a model of individual knowledge profiles and community affiliation. These factors suggest specific conditions under which improved access, search, and screening can either integrate or fragment interaction on various dimensions. As IT capabilities continue to improve, preferences--not geography or technology--become the key determinants of community boundaries.  Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. Starting from a few labeled articles and users, the algorithms propagate political leaning labels to the entire graph. In cross-validation, the best algorithm achieved 99.6 accuracy on held-out users and 96.3 accuracy on held-out articles. Adding social data such as users’ friendship or text features such as cosine similarity did not improve accuracy. The propagation algorithms, using the subjective liking data from users, also performed better than an SVM based text classifier, which achieved 92.0 accuracy on articles. It is not uncommon for certain social networks to divide into two opposing camps in response to stress. This happens, for example, in networks of political parties during winner-takes-all elections, in networks of companies competing to establish technical standards, and in networks of nations faced with mounting threats of war. A simple model for these two-sided separations is the dynamical system dX dt = X2, where X is a matrix of the friendliness or unfriendliness between pairs of nodes in the network. Previous simulations suggested that only two types of behavior were possible for this system: Either all relationships become friendly or two hostile factions emerge. Here we prove that for generic initial conditions, these are indeed the only possible outcomes. Our analysis yields a closed-form expression for faction membership as a function of the initial conditions and implies that the initial amount of friendliness in large social networks (started from random initial conditions) determines whether they will end up in intractable conflict or global harmony. VWhile dimensional studies of congressional voting find a single, ideological dimension, regression estimates find several constituency and party dimensions in addition to ideology. I rescale several unidimensional studies to show their increased classification success over the null hypothesis that votes are not unidimensional. Several null hypotheses are explored. With these null hypotheses, 66 -75 of nonunidimensional roll call votes are nevertheless correctly classified by one dimension. After the resealing, one dimension succeeds in correctly classifying 25 -50 of the votes, and second and third dimensions are important. New communications technologies offer important opportunities for revitalizing American democracy. They encourage broader issue discussions, greater specificity in candidate positions, and positive messages rather than negative ones. New communications systems can begin to uncouple wealth from voter impressions, make candidate messages available in multiple formats and languages, and encourage two-way communications: from candidate to candidate, from voter to candidate, and from voter to voter. 