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Blog cascade affinity: analysis and prediction

Published: 02 November 2009 Publication History

Abstract

Information propagation within the blogosphere is of much importance in implementing policies, marketing research, launching new products, and other applications. In this paper, we take a microscopic view of the information propagation pattern in blogosphere by investigating blog cascade affinity. A blog cascade is a group of posts linked together discussing about the same topic, and cascade affnity refers to the phenomenon of a blog's inclination to join a specific cascade. We identify and analyze an array of features that may affect a blogger's cascade joining behavior and utilize these features to predict cascade affinity of blogs. Evaluated on a real dataset consisting of 873,496 posts, our svm-based prediction achieved accuracy of 0.723 measured by F1. Our experiments also showed that among all features identified, the number of friends was the most important factor affecting bloggers' inclination to join cascades.

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cover image ACM Conferences
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
November 2009
2162 pages
ISBN:9781605585123
DOI:10.1145/1645953
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2009

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Author Tags

  1. blog cascade
  2. information flow
  3. network evolution
  4. social networks

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Cited By

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  • (2022)A Comprehensive Survey on Affinity Analysis, Bibliomining, and Technology Mining: Past, Present, and Future ResearchApplied Sciences10.3390/app1210522712:10(5227)Online publication date: 21-May-2022
  • (2018)Time- and Event-Driven Modeling of Blogger InfluenceEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_378(3101-3113)Online publication date: 12-Jun-2018
  • (2017)A comparative analysis of structural graph metrics to identify anomalies in online social networksComputers and Electrical Engineering10.1016/j.compeleceng.2016.11.01857:C(294-310)Online publication date: 1-Jan-2017
  • (2017)Time- and Event-Driven Modeling of Blogger InfluenceEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_378-1(1-13)Online publication date: 16-Jun-2017
  • (2015)Community reactionProceedings of the 2nd ACM IKDD Conference on Data Sciences10.1145/2732587.2732596(69-74)Online publication date: 18-Mar-2015
  • (2015)Social Influence Study in Online Networks: A Three-Level ReviewJournal of Computer Science and Technology10.1007/s11390-015-1512-730:1(184-199)Online publication date: 21-Jan-2015
  • (2014)Affinity-driven blog cascade analysis and predictionData Mining and Knowledge Discovery10.1007/s10618-013-0307-028:2(442-474)Online publication date: 1-Mar-2014
  • (2014)Data MiningEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-6170-8_56(332-341)Online publication date: 5-Oct-2014
  • (2014)Time- and Event-Driven Modeling of Blogger InfluenceEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-6170-8_378(2154-2165)Online publication date: 5-Oct-2014
  • (2013)Simulating the spread of opinions in online social networks when targeting opinion leadersInformation Systems and e-Business Management10.1007/s10257-012-0210-z11:4(597-621)Online publication date: 1-Dec-2013
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