Identifying the influential bloggers in a community

By Nitin Agarwal, Huan Liu, Lei Tang, Philip S. Yu. Web Search and Data Mining, 2008

567 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bcf9e767b046b9f4b655508e2ce3f10fc016d490

How researchers see this paper

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In Artificial Intelligence

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Business Intelligence in Blogs: Understanding Consumer Interactions and Communities (2012, background): “Some studies have attempted to identify the bloggers who are the most important in the information dissemination process using graph algorithms (Agarwal et al. 2008; Mathioudakis and Koudas 2009).”
Recommender Systems (2012, background): “They are found in a wide range of systems, including product reviews [221, 222], e-mails [223], blogs [224] and microblogs [225].”
Using a model of social dynamics to predict popularity of news (2010, background): “Is it item’s inherent quality [2], consumer response to it [5], or some external factors, such as social influence [15, 18, 16]? In a landmark study, Salganik et al.”
Trends in Social Media: Persistence and Decay (2011, background): “Agarwal et al. [1] studied the problem of identifying inﬂuential bloggers in the blogosphere.”
Open challenges for data stream mining research (2014, background): “[1] C.”
Social Network Analysis and Mining for Business Applications (2011, methodology): “Other applications include personalized recommendations [Song et al. 2006, 2007] and feed ranking in social networks [Samper et al. 2006]. Besides, patterns of influence can be taken as a sign of user trust and exploited for computing trust propagation in large networks and in P2P systems [Guha et a”
Influence and passivity in social media (2010, background): “Agarwal et al. [8] have examined the problem of identifying influential bloggers in the blogosphere.”
Community Detection and Mining in Social Media (2010, background): “This change opens up opportunities to fuse various sources of information to study centrality (Agarwal et al., 2008).”
A classification for community discovery methods in complex networks (2011, result): “The general goal is similar to recent works such as [143, 144, 145].”