The political blogosphere and the 2004 U.S. election: divided they blog

By Lada A. Adamic, Natalie S. Glance. LinkKDD '05, 2005

3,071 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2d679de19dd1a04b49a45c42e32697e8779cde0c

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Citations in the corpus

Social media and political communication: a social media analytics framework (2012, background): “In an empirical work, Adamic and Glance (2005) study the linking patterns and discussion topics of US political bloggers and provide evidence for a divided blogosphere: liberals and conservatives primarily link within their separate communities, with far fewer cross-links exchanged between them.”
Blogging Practices: An Analytical Framework (2007, background): “With respect to political topics, several studies have shown that differences in affiliation with ideological camps manifest in distinct sub-clusters within the blogosphere (Adamic & Glance, 2005; Tremayne et al., 2006).”
On the Behaviour of Deviant Communities in Online Social Networks (2016, background): “The degree of inter-community interaction has been analyzed mostly in the context of heavily polarized networks, the most classical example being online discussions between two opposing political views (Adamic and Glance 2005; Conover et al. 2011; Feller et al. 2011).”
Discrete Signal Processing on Graphs (2012, background): “Consider a set of N = 1224 political blogs on the Web that we wish to classify as “conservative” or “liberal” based on the ir context [69].”
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel (2013, background): “Experiment 2 generates networks from the standard SBM. Finally, the benefits of regularization are illustrated on an empirical network from the political blogosphere during the 2004 presidential election (Adamic and Glance [17]).”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, background): “While there have been prior attempts to infer the political bias of textual content in online forums, such as blogs and news articles [1, 46, 51] or hashtags on Twitter [44], to our knowledge, no work has measured the political bias of a tweet based on its textual content, particularly due to its ve”
Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter (2016, positive): “…social media measures network structure but not communications (e.g., Colleoni et al., 2014) or communications but not network structure (e.g., Adamic and Glance, 2005).4 As emphasized by our model, measuring exposure to information in social networks requires measures of both network…”
Patterns of Cascading Behavior in Large Blog Graphs (2007, background): “election, as they became sources for campaign fundraising as well as an important supplement to the mainstream media [1].”
Social Clicks: What and Who Gets Read on Twitter? (2016, neutral): “Information diffusion naturally shapes collective opinion and consensus [12, 7, 18], hence the role of social media in redistributing influence online has been under scrutiny ever since blogs and email chains [2, 17].”
Community Preserving Network Embedding (2017, background): “Political blog network (Polblogs)4 (Adamic and Glance 2005) (1222 nodes, 16715 edges) is composed of blogs about US politics and the web links between them, recorded in 2005.”
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment (2010, background): “, Böhringer and Richter 2009) or the analysis of Twitter as electronic word of mouth in the area of product marketing (e.g., Jansen et al. 2009). In their study, Jansen et al. (2009) have found that 19% of a random sample of tweets contained mentions of a brand or product and that an automated class”