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Discovering information diffusion paths from blogosphere for online advertising

Authors:

Avaré Stewart,

Wolfgang NejdlAuthors Info & Claims

ADKDD '07: Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising

Pages 46 - 54

https://doi.org/10.1145/1348599.1348607

Published: 12 August 2007 Publication History

Abstract

Allowing global distribution of information to large audiences at very low cost, the Internet has emerged as a vital medium for marketing and advertising. Weblogs, a new form of self publication on the Internet, have attracted online advertisers because of their incredible growth-rate in recent years. In this paper, we propose to discover information diffusion paths from the blogosphere to track how information frequently flows from blog to blog. This knowledge can be used in various applications of online campaign. Our approach is based on analyzing the content of blogs. After detecting trackable topics of blogs, we model a blog community as a blog sequence database. Then, the discovery of information diffusion paths is formalized as a problem of frequent pattern mining. We develop a new data mining algorithm to discover information diffusion paths. Experiments conducted on real life dataset show that our algorithm discovers information diffusion paths efficiently. The discovered information diffusion paths are accurate in predicting the future information flow in the blog community.

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Cited By

Agarwal NLiu H(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
https://doi.org/10.1007/978-1-4939-7131-2_378
Agarwal NLiu H(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
https://doi.org/10.1007/978-1-4614-7163-9_378-1
Li HBhowmick SSun ACui J(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
https://dl.acm.org/doi/10.1007/s10618-013-0307-0
Show More Cited By

Index Terms

Discovering information diffusion paths from blogosphere for online advertising
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

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Published In

cover image ACM Conferences

ADKDD '07: Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising

August 2007

75 pages

ISBN:9781595938336

DOI:10.1145/1348599

General Chair:
Ying Li
adCenter Labs, Microsoft Corporation

Copyright © 2007 ACM.

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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Publication History

Published: 12 August 2007

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KDD07

Sponsor:

KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12, 2007

California, San Jose

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Overall Acceptance Rate 12 of 21 submissions, 57%

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Cited By

Agarwal NLiu H(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
https://doi.org/10.1007/978-1-4939-7131-2_378
Agarwal NLiu H(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
https://doi.org/10.1007/978-1-4614-7163-9_378-1
Li HBhowmick SSun ACui J(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
https://dl.acm.org/doi/10.1007/s10618-013-0307-0
Agarwal NMahata DLiu H(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
https://doi.org/10.1007/978-1-4614-6170-8_378
Bhat SAbulaish M(2013)Analysis and mining of online social networksWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11053:6(408-444)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1002/widm.1105
Guolin Niu Li VYi Long Xu K(2012)Measurement-driven temporal analysis of information diffusion in online social networks2012 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2012.6503419(2060-2065)Online publication date: Dec-2012
https://doi.org/10.1109/GLOCOM.2012.6503419
Li CKuo THo CHong SLin WLin S(2012)Modeling and evaluating information propagation in a microblogging social networkSocial Network Analysis and Mining10.1007/s13278-012-0082-83:3(341-357)Online publication date: 5-Sep-2012
https://doi.org/10.1007/s13278-012-0082-8
Zhou XXu YLi YJosang ACox C(2012)The state-of-the-art in personalized recommender systems for social networkingArtificial Intelligence Review10.1007/s10462-011-9222-137:2(119-132)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1007/s10462-011-9222-1
Guan XZhou YZheng QSun QZhao J(2011)Dynamic Models and Analysis for Information Propagation in Online Social NetworksSocio-Technical Networks10.1201/b10327-4(39-69)Online publication date: 23-May-2011
https://doi.org/10.1201/b10327-4
Kuo THung SLin WLin SPeng TShih C(2011)Assessing the Quality of Diffusion Models Using Real-World Social Network DataProceedings of the 2011 International Conference on Technologies and Applications of Artificial Intelligence10.1109/TAAI.2011.42(200-205)Online publication date: 11-Nov-2011
https://dl.acm.org/doi/10.1109/TAAI.2011.42
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