Explorando a complementaridade entre estratégias para detecção de usuários influentes no Twitter
Abstract
The so-called influencers have an important role on the information diffusion in social media environments, since they might dictate peer-to-peer recommendation, impacting tasks such as brand evaluation, advertising, etc. Despite the growing number of works that identify influencers by exploiting distinct information, deciding on the best strategy for each domain is complex. In this work, we perform a quantitative study among some of the main strategies for identifying influencers. As main contributions, we highlight a better understanding about the selected strategies and a novel and effective meta-learning approach, based on PCA, that is able to combine linearly distinct strategies.
References
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Bonacich, P. and Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3):191–201.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P. K. (2010). Measuring user influence in twitter: The million follower fallacy. ICWSM.
Davis, C. (1962). The norm of the schur product operation. Numerische Mathematik.
Fagin, R., Kumar, R., and Sivakumar, D. (2003). Comparing top k lists. In Proc. 14th ACM-SIAM 2003.
Galeotti, A. and Goyal, S. (2010). The law of the few. The American Economic Review.
Golub, G. H. and Loan, C. F. V. (1996). Matrix Computations. Johns Hopkins University Press.
Ilyas, M. U. and Radha, H. (2011). Identifying influential nodes in online social networks using principal component centrality. In Communications (ICC), IEEE International Conference. IEEE.
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., and Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics.
Lee, C., Kwak, H., Park, H., and Moon, S. (2010). Finding influentials based on the temporal order of information adoption in twitter. In Proceedings of the 19th International Conference on World Wide Web. ACM.
Li, Y.-M., Lin, C.-H., and Lai, C.-Y. (2010). Identifying influential reviewers for wordof- mouth marketing. Electronic Commerce Research and Applications, (4).
McCown, F. and Nelson, M. L. (2007). Agreeing to disagree: Search engines and their public interfaces. In Proc. 7th ACM/IEEE-CS JCDL 2007. ACM.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine.
Silva, A., Guimarães, S., Meira, Jr., W., and Zaki, M. (2013). Profilerank: Finding relevant content and influential users based on information diffusion. In Proceedings of the 7th Workshop on Social Network Mining and Analysis. ACM.
Subbian, K., Sharma, D., Wen, Z., and Srivastava, J. (2013). Finding influencers in networks using social capital. In Proc. of 2013 IEEE/ACM ASONAM.
Wu, S., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Who says what to whom on twitter. In Proc. the 20th WWW 2011.
Published
2015-07-20
How to Cite
NEVES, Alan; VIEIRA, Ramon; MOURÃO, Fernando; ROCHA, Leonardo.
Explorando a complementaridade entre estratégias para detecção de usuários influentes no Twitter. In: SBC UNDERGRADUATE RESEARCH CONTEST (CTIC-SBC), 34. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 31-40.
