The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Identifying the influential bloggers in a community
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
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Cites
The long tail: why the future of business is selling less of more
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2783 · Cited by 2,869
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Social Media Mining: An Introduction
Zafarani, R., Abbasi, M. A., & Liu, H. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088510 · Cited by 592
Self-citationInfluence and Passivity in Social Media
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-23808-6_2 · Cited by 579
A classification for community discovery methods in complex networks
Coscia, M., Giannotti, F., & Pedreschi, D. (2011) · Statistical Analysis and Data Mining: The ASA Data Science Journal · doi:10.1002/sam.10133 · Cited by 353
Business Intelligence in BLOGS: Understanding Consumer Interactions and Communities1
Chau, M., & Xu, J. (2012) · MIS Quarterly · doi:10.2307/41703504 · Cited by 347
Influence analysis in social networks: A survey
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.01.005 · Cited by 308
Metadata
- Authors
- Agarwal, N., Liu, H., Tang, L., & Yu, P. S.
- Venue
- Proceedings of the international conference on Web search and web data mining - WSDM '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 207
- OpenAlex
- W2098479684
- Cited by
- 511
- References
- 1
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Agarwal, N., Liu, H., Tang, L., & Yu, P. S. (2008). Identifying the influential bloggers in a community. Proceedings of the international conference on Web search and web data mining - WSDM '08. https://doi.org/10.1145/1341531.1341559
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.