ACM Home Page
Please provide us with feedback. Feedback
Identifying the influential bloggers in a community
Full text PdfPdf (387 KB)
Source
Web Search and Web Data Mining archive
Proceedings of the international conference on Web search and web data mining table of contents
Palo Alto, California, USA
SESSION: Social search table of contents
Pages 207-218  
Year of Publication: 2008
ISBN:978-1-59593-927-9
Authors
Nitin Agarwal  Arizona State University, Tempe, AZ
Huan Liu  Arizona State University, Tempe, AZ
Lei Tang  Arizona State University, Tempe, AZ
Philip S. Yu  University of Illinois at Chicago, Chicago, IL
Sponsors
ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 70,   Downloads (12 Months): 570,   Citation Count: 9
Additional Information:

abstract   references   cited by   index terms   review   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1341531.1341559
What is a DOI?

ABSTRACT

Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their virtual communities of similar interests. Activities happened in Blogosphere affect the external world. One way to understand the development on Blogosphere is to find influential blog sites. There are many non-influential blog sites which form the "the long tail". Regardless of a blog site being influential or not, there are influential bloggers. Inspired by the high impact of the influentials in a physical community, we study a novel problem of identifying influential bloggers at a blog site. Active bloggers are not necessarily influential. Influential bloggers can impact fellow bloggers in various ways. In this paper, we discuss the challenges of identifying influential bloggers, investigate what constitutes influential bloggers, present a preliminary model attempting to quantify an influential blogger, and pave the way for building a robust model that allows for finding various types of the influentials. To illustrate these issues, we conduct experiments with data from a real-world blog site, evaluate multi-facets of the problem of identifying influential bloggers, and discuss unique challenges. We conclude with interesting findings and future work


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
2
 
3
T. Coffman and S. Marcus. Dynamic classification of groups through social network analysis and HMMs. In Proceedings of IEEE Aerospace Conference, 2004.
 
4
Daniel Drezner and Henry Farrell. The power and politics of blogs. In American Political Science Association Annual Conference, 2004.
 
5
T. Elkin. Just an online minute. . . online forecast. http://publications.mediapost.com/index.cfm?fuseaction=Articles.showArticle art aid=29803.
 
6
Gerald D. Fensterer. Planning and Assessing Stability Operations: A Proposed Value Focus Thinking Approach. PhD thesis, Air Force Institute of Technology, 2007.
7
 
8
Kathy E. Gill. How can we measure the influence of the blogosphere? In Proceedings of the WWW'04: workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2004.
 
9
10
11
 
12
Akshay Java, Pranam Kolari, Tim Finin, and Tim Oates. Modeling the spread of influence on the blogosphere. In Proceedings of the 15th International World Wide Web Conference, 2006.
 
13
R. L. Keeney and H. Raiffa. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press, 1993.
 
14
Ed Keller and Jon Berry. One American in ten tells the other nine how to vote, where to eat and, what to buy. They are The Influentials. The Free Press, 2003.
15
 
16
 
17
P. Kolari, T. Finin, and A. Joshi. SVMs for the blogosphere: Blog identification and splog detection. In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, 2006.
18
19
 
20
Yu-Ru Lin, Hari Sundaram,Yun Chi, Jun Tatemura, and Belle Tseng. Discovery of blog communities based on mutual awareness. In Proceedings of the 3rd annual workshop on webloging ecosystem: aggreation, analysis and dynamics, 2006.
21
22
 
23
Tim O'Reilly. What is Web 2.0-design patterns and business models for the next generation of software. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20. html, September 2005.
 
24
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.
25
 
26
 
27
Mike Thelwall. Bloggers under the London attacks: Top information sources and topics. In Proceedings of the 3rd annual workshop on webloging ecosystem: aggreation, analysis and dynamics, 2006.
28

CITED BY  10


REVIEW

"Karthik Ramachandran : Reviewer"

A novel and interesting question is considered in this paper: How do you identify influential bloggers within a community blog? There are basically two types of blogs: individual and community. Individual blogs are written by a single author, whil  more...

Collaborative Colleagues:
Nitin Agarwal: colleagues
Huan Liu: colleagues
Lei Tang: colleagues
Philip S. Yu: colleagues