The political blogosphere and the 2004 U.S. election

Adamic, Lada A.; Glance, Natalie S.

doi:10.1145/1134271.1134277

Cited by 2,106 publications

(1,693 citation statements)

References 5 publications

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“…The distribution of social degree centrality has benefited from a strong interest in the literature, especially in the very case of scientific networks; first, indirectly, by Lotka (1926) through the distribution of the number of published papers and by de Solla Price (1965) through the number of cited paper (Subramanyam, 1983) -then, more recently, by Barabási et al (2002), Newman (2004) and Redner (2005). Hindman et al (2003) and Adamic and Glance (2005) have carried similar studies in the case of political opinion websites. …”

Section: Heterogeneity Of Social and Semantic Capitalsmentioning

confidence: 99%

Social and semantic coevolution in knowledge networks

2010

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Socio-semantic networks involve agents creating and processing information: communities of scientists, software developers, wiki contributors and webloggers are, among others, examples of such knowledge networks. We aim at demonstrating that the dynamics of these communities can be adequately described as the coevolution of a social and a socio-semantic network. More precisely, we will first introduce a theoretical framework based on a social network and a socio-semantic network, i.e. an epistemic network featuring agents, concepts and links between agents and between agents and concepts. Adopting a relevant empirical protocol, we will then describe the joint dynamics of social and socio-semantic structures, at both macroscopic and microscopic scales, emphasizing the remarkable stability of these macroscopic properties in spite of a vivid local, agentbased network dynamics.

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Section: Heterogeneity Of Social and Semantic Capitalsmentioning

confidence: 99%

Social and semantic coevolution in knowledge networks

2010

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“…Most work on extracting cascades from large-scale on-line data has been done in the blog domain [1,7,10]. The authors in this domain note that, while information propagates between blogs, examples of genuine cascading behavior appeared relatively rare.…”

Section: Related Workmentioning

confidence: 99%

“…Associated with each recommendation is the product involved, and the time the recommendation was made. Studies of blogspace either spend a lot of effort mining topics from posts [2,7] or consider only the properties of blogspace as a graph of unlabeled URLs [1,10]. Temporally evolving graphs are explored in [4].…”

Section: Related Workmentioning

confidence: 99%

Patterns of Influence in a Recommendation Network

Leskovec

Singh

Kleinberg

2006

Advances in Knowledge Discovery and Data Mining

264

174

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Abstract. Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks. We investigate a large person-to-person recommendation network, consisting of four million people who made sixteen million recommendations on half a million products. Such a dataset allows us to pose a number of fundamental questions: What kinds of cascades arise frequently in real life? What features distinguish them? We enumerate and count cascade subgraphs on large directed graphs; as one component of this, we develop a novel efficient heuristic based on graph isomorphism testing that scales to large datasets. We discover novel patterns: the distribution of cascade sizes is approximately heavy-tailed; cascades tend to be shallow, but occasional large bursts of propagation can occur. The relative abundance of different cascade subgraphs suggests subtle properties of the underlying social network and recommendation process.

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“…Recently, has been applied to US political blogs (e.g., Adamic and Glance 2005;Lazer et al 2010;Hanneman and Shelton 2011;Rosen et al 2011), where there is a strong tendency for conservative bloggers to link to conservative material, whereas more liberal bloggers spread their links out. 3 Clearly, a relevant observation when trying to understand the context and semantics of links.…”

Section: Related Workmentioning

confidence: 99%

“…However, as the data have become increasingly available over recent years, researchers in computer science, physics, social sciences, and more have started to analyze these networks to identify network characteristics, communities, behaviors, influencers, etc. (see, e.g., Adamic and Glance 2005;Leskovec et al 2007b;Joshi et al 2007;Nallapati and Cohen 2008;Agarwal and Liu 2008;Hearst and Dumais 2009).…”

Section: Introductionmentioning

confidence: 99%

Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis

Macskassy

2011

Soc. Netw. Anal. Min.

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The last decade has seen an explosion in blogging and the blogosphere is continuing to grow, having a large global reach and many vibrant communities. Researchers have been pouring over blog data with the goal of finding communities, tracking what people are saying, finding influencers, and using many social network analytic tools to analyze the underlying social networks embedded within the blogosphere. One of the key technical problems with analyzing large social networks such as those embedded in the blogosphere is that there are many links between individuals and we often do not know the context or meaning of those links. This is problematic because it makes it difficult if not impossible to tease out the true communities, their behavior, how information flows, and who the central players are (if any). This paper seeks to further our understanding of how to analyze large blog networks and what they can tell us. We analyze 1.13M blogs posted by 185K bloggers over a period of 3 weeks. These bloggers span private blog sites through large blog-sites such as LiveJournal and Blogger. We show that we can, in fact, tag links in meaningful ways by leveraging topic-detection over the blogs themselves. We use these topics to contextually tag links coming from a particular blog post. This enrichment enables us to create smaller topic-specific graphs which we can analyze in some depth. We show that these topic-specific graphs not only have a different topology from the general blog graph but also enable us to find central bloggers which were otherwise hard to find. We further show that a temporal analSofus A. Macskassy Fetch Technologies 841 Apollo Street, Suite 400 El Segundo, CA 90245, USA Tel: +310-414-9849 E-mail: sofmac@fetch.com ysis identifies behaviors in terms of how components form as well as how bloggers continue to link after components form. These behaviors come to light when doing an analysis on the topic-specific graphs but are hidden or not easily discernable when analyzing the general blog graph.

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The political blogosphere and the 2004 U.S. election

Cited by 2,106 publications

References 5 publications

Social and semantic coevolution in knowledge networks

Social and semantic coevolution in knowledge networks

Patterns of Influence in a Recommendation Network

Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis

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