PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

Working towards a topology for politician retweet networks in 23 different countries using network measures.
No caption available
… 
No caption available
… 
No caption available
… 
Content may be subject to copyright.
A preview of the PDF is not available
ResearchGate has not been able to resolve any citations for this publication.
Conference Paper
Full-text available
The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we describe several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections. Using a data set of 1,000 manually-annotated individuals, we find that a support vector machine (SVM) trained on hash tag metadata outperforms an SVM trained on the full text of users' tweets, yielding predictions of political affiliations with 91% accuracy. Applying latent semantic analysis to the content of users' tweets we identify hidden structure in the data strongly associated with political affiliation, but do not find that topic detection improves prediction performance. All of these content-based methods are outperformed by a classifier based on the segregated community structure of political information diffusion networks (95% accuracy). We conclude with a practical application of this machinery to web-based political advertising, and outline several approaches to public opinion monitoring based on the techniques developed herein.
Article
Full-text available
We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.
Article
Media outlets and pundits have been quick to embrace online social networks to disseminate their own opinions. But pundits' opinions and news coverage are often marked by a clear political bias, as widely evidenced during the fiercely contested 2012 U.S. presidential elections. Given the wide availability of such data from sites like Twitter, a natural question is whether we can quantify the political leanings of media outlets using OSN data. In this work, by drawing a correspondence between tweeting and retweeting behavior, we formulate political leaning estimation as an ill-posed linear inverse problem. The result is a simple and scalable approach that does not require explicit knowledge of the network topology. We evaluate our method with a dataset of 119 million election-related tweets collected from April to November, and use it to study the political leaning of prominent tweeters and media sources. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Article
The use of Twitter by politicians, parties, and the general audience in politics, particularly during election campaigns, has become an extremely popular research field almost overnight. Even though Twitter, a medium that emerged early in 2006 – the first tweet was posted on 21 March 2006 by Jack Dorsey, co-founder of Twitter – and elections occurring only every few years, it has already received much academic attention. The studies produced are very diverse, ranging from analyzing how politicians or citizens use Twitter, to looking at their activities and the content of political Twitter messages, to network studies of Twitter users. This review will cover many types of studies that characterize the field. The large diversity in the studies conducted on elections will be represented in this review of approaches.
Article
This study investigates the content characteristics of Twitter during an election campaign, and the relationship between candidates’ style of online campaigning (i.e., politically personalized and interactive communication) and electoral support for those candidates. Thereby, it provides a better understanding of the linkage between the use of Twitter by candidates and effects on preferential votes. Two data sources are used to examine this relationship: first, a quantitative computer-assisted as well as a manual content analysis of tweets posted by political candidates during the Dutch national elections of 2010 (N = 40,957) and second, a dataset containing the number of votes for electable political candidates during that period. The findings show that using Twitter has positive consequences for political candidates. Candidates who used Twitter during the course of the campaign received more votes than those who did not, and using Twitter in an interactive way had a positive impact as well.
In this updated and expanded edition of his classic text, Arend Lijphart offers a broader and deeper analysis of worldwide democratic institutions than ever before. Examining thirty-six democracies during the period from 1945 to 2010, Lijphart arrives at important-and unexpected-conclusions about what type of democracy works best. Praise for the previous edition: "Magnificent...The best-researched book on democracy in the world today."-Malcolm Mackerras, American Review of Politics "I can't think of another scholar as well qualified as Lijphart to write a book of this kind. He has an amazing grasp of the relevant literature, and he's compiled an unmatched collection of data."-Robert A. Dahl, Yale University "This sound comparative research ...will continue to be a standard in graduate and undergraduate courses in comparative politics."-Choice
Partisans without Constraint: Political Polarization and Trends in American Public Opinion
  • D Baldassarri
  • A Gelman
Baldassarri, D., & Gelman, A. (2008, 09). Partisans without Constraint: Political Polarization and Trends in American Public Opinion. American Journal of Sociology, 114(2), 408-446. doi:10.1086/590649