research-article

A probabilistic graphical model for brand reputation assessment in social networks

Authors:

Zhengzhang Chen,

Alok ChoudharyAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 223 - 230

https://doi.org/10.1145/2492517.2492556

Published: 25 August 2013 Publication History

Abstract

Social media has become a popular platform that connects people who share information, in particular personal opinions. Through such a fast information exchange mechanism, reputation of individuals, consumer products, or business companies can be quickly built up within a social network. Recently, applications mining social network data start emerging to find the communities sharing the same interests for marketing purposes. Knowing the reputation of social network entities, such as celebrities or business companies, can help develop better strategies for election campaigns or new product advertisements. In this paper, we propose a probabilistic graphical model to collectively measure reputations of entities in social networks. By collecting and analyzing large amount of user activities on Facebook, our model can effectively and efficiently rank entities, such as presidential candidates, professional sport teams, musician bands, and companies, based on their social reputation. The proposed model produces results largely consistent with the two publicly available systems - movie ranking in Internet Movie Database and business school ranking by the US news & World Report - with the correlation coefficients of 0.75 and -0.71, respectively.

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Cited By

Fradi ALouhichi BMahjoub MEynard B(2021)A new approach for reusable 3D CAD objects detection, by similarity calculation based on Bayesian network models (BNM)International Journal of Computer Integrated Manufacturing10.1080/0951192X.2021.1972457(1-20)Online publication date: 13-Sep-2021
https://doi.org/10.1080/0951192X.2021.1972457
Kim K(2021)On principal graphical models with application to Gene networkComputational Statistics & Data Analysis10.1016/j.csda.2021.107344(107344)Online publication date: Sep-2021
https://doi.org/10.1016/j.csda.2021.107344
Odumuyiwa VOsisiogu U(2019)A Systematic Review on Hidden Markov Models for Sentiment Analysis2019 15th International Conference on Electronics, Computer and Computation (ICECCO)10.1109/ICECCO48375.2019.9043297(1-7)Online publication date: Dec-2019
https://doi.org/10.1109/ICECCO48375.2019.9043297
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A probabilistic graphical model for brand reputation assessment in social networks

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 ACM.

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Published: 25 August 2013

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ASONAM '13

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SIGKDD

ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Fradi ALouhichi BMahjoub MEynard B(2021)A new approach for reusable 3D CAD objects detection, by similarity calculation based on Bayesian network models (BNM)International Journal of Computer Integrated Manufacturing10.1080/0951192X.2021.1972457(1-20)Online publication date: 13-Sep-2021
https://doi.org/10.1080/0951192X.2021.1972457
Kim K(2021)On principal graphical models with application to Gene networkComputational Statistics & Data Analysis10.1016/j.csda.2021.107344(107344)Online publication date: Sep-2021
https://doi.org/10.1016/j.csda.2021.107344
Odumuyiwa VOsisiogu U(2019)A Systematic Review on Hidden Markov Models for Sentiment Analysis2019 15th International Conference on Electronics, Computer and Computation (ICECCO)10.1109/ICECCO48375.2019.9043297(1-7)Online publication date: Dec-2019
https://doi.org/10.1109/ICECCO48375.2019.9043297
Buitelaar PWood INegi SArcan MMcCrae JAbele ARobin CAndryushechkin VZiad HSagha HSchmitt MSchuller BSanchez-Rada JIglesias CNavarro CGiefer AHeise NMasucci VDanza FCaterino CSmrz PHradis MPovolny FKlimes MMatejka PTummarello G(2018)MixedEmotionsIEEE Transactions on Multimedia10.1109/TMM.2018.279828720:9(2454-2465)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1109/TMM.2018.2798287
Yue KWu HFu XXu JYin ZLiu W(2017)A data-intensive approach for discovering user similarities in social behavioral interactions based on the bayesian networkNeurocomputing10.1016/j.neucom.2016.09.042219:C(364-375)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.042
Xie YChen ZAgrawal AChoudhary ALiu LHe QIyengar ANejdl WPei JRastogi R(2013)Random walk-based graphical sampling in unbalanced heterogeneous bipartite social graphsProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507822(1473-1476)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507822

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