research-article

Using Centrality Measures to Predict Helpfulness-Based Reputation in Trust Networks

Authors:

Pasquale De Meo,

Katarzyna Musial-Gabrys,

Domenico Rosaci,

Giuseppe M. L. Sarnè,

Lora AroyoAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 17, Issue 1

Article No.: 8, Pages 1 - 20

https://doi.org/10.1145/2981545

Published: 25 February 2017 Publication History

Abstract

In collaborative Web-based platforms, user reputation scores are generally computed according to two orthogonal perspectives: (a) helpfulness-based reputation (HBR) scores and (b) centrality-based reputation (CBR) scores. In HBR approaches, the most reputable users are those who post the most helpful reviews according to the opinion of the members of their community. In CBR approaches, a “who-trusts-whom” network—known as a trust network—is available and the most reputable users occupy the most central position in the trust network, according to some definition of centrality. The identification of users featuring large HBR scores is one of the most important research issue in the field of Social Networks, and it is a critical success factor of many Web-based platforms like e-marketplaces, product review Web sites, and question-and-answering systems. Unfortunately, user reviews/ratings are often sparse, and this makes the calculation of HBR scores inaccurate. In contrast, CBR scores are relatively easy to calculate provided that the topology of the trust network is known. In this article, we investigate if CBR scores are effective to predict HBR ones, and, to perform our study, we used real-life datasets extracted from CIAO and Epinions (two product review Web sites) and Wikipedia and applied five popular centrality measures—Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank and Eigenvector Centrality—to calculate CBR scores. Our analysis provides a positive answer to our research question: CBR scores allow for predicting HBR ones and Eigenvector Centrality was found to be the most important predictor. Our findings prove that we can leverage trust relationships to spot those users producing the most helpful reviews for the whole community.

References

[1]

J. Alstott, E. Bullmore, and D. Plenz. 2014. Powerlaw: A python package for analysis of heavy-tailed distributions. PloS ONE 9, 1 (2014), e85777.

[2]

L. Backstrom and J. Leskovec. 2011. Supervised random walks: Predicting and recommending links in social networks. In Proc. of the International Conference on Web search and Data Mining (WSDM’11). ACM, 635--644.

Digital Library

[3]

E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. 2012. The role of social networks in information diffusion. In Proc. of the International Conference on World Wide Web (WWW’12). ACM, 519--528.

Digital Library

[4]

A. Bavelas. 1948. A mathematical model for group structures. Hum. Organiz. 7, 3 (1948), 16--30.

[5]

A. Berman and R. Plemmons. 1994. Nonnegative Matrices in the Mathematical Sciences. SIAM.

[6]

C. Bishop. 2006. Pattern Recognition and Machine Learning. Springer.

Digital Library

[7]

P. Bonacich and P. Lloyd. 2001. Eigenvector-like measures of centrality for asymetric relations. Soc. Netw. 23, 3 (2001), 191--201.

[8]

F. Bonchi, C. Castillo, A. Gionis, and A. Jaimes. 2011. Social network analysis and mining for business applications. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 22.

Digital Library

[9]

S. Brin and L. Page. 1998. The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 30, 1--7 (1998), 107--117.

Digital Library

[10]

R. Burt. 1992. Structural Holes: The Social Structure of Competition. Harvard University Press.

[11]

B. Butler, L. Sproull, S. Kiesler, and R. Kraut. 2007. Community effort in online groups: Who does the work and why. In Leadership at a Distance: Research in Technologically Supported Work, S. Weisband and L. Atwater (Eds.). Lawrence Erlbaum, 171--194.

[12]

J. Chen, C. Zhang, and Y. Xu. 2009. The role of mutual trust in building members’ loyalty to a C2C platform provider. Int. J. Electron. Comm. 14, 1 (2009), 147--171.

Digital Library

[13]

P. Chirita, W. Nejdl, M. Schlosser, and O. Scurtu. 2004. Personalized reputation management in P2P networks. In Proc. of the International Workshop on Trust, Security, and Reputation on the Semantic Web. Springer.

Digital Library

[14]

A. Clauset, C. Shalizi, and M. Newman. 2009. Power-law distributions in empirical data. SIAM Rev. 51, 4 (2009), 661--703.

Digital Library

[15]

G. Van de Bunt, R. Wittek, and M. de Klepper. 2005. The evolution of intra-organizational trust networks the case of a German paper factory: An empirical test of six trust mechanisms. Int. Sociol. 20, 3 (2005), 339--369.

[16]

P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti. 2014. Mixing local and global information for community detection in large networks. J. Comput. Syst. Sci. 80, 1 (2014), 72--87.

Digital Library

[17]

P. De Meo, E. Ferrara, D. Rosaci, and G. M. L. Sarnè. 2015. Trust and compactness in social network groups. IEEE T. Cybernet. 45, 2 (2015), 205--216.

[18]

P. De Meo, A. Nocera, G. Terracina, and D. Ursino. 2011. Recommendation of similar users, resources and social networks in a social internetworking scenario. Inform. Sci. 181, 7 (2011), 1285--1305.

Digital Library

[19]

J. Elith, J. Leathwick, and T. Hastie. 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77, 4 (2008), 802--813.

[20]

T. La Fond and J. Neville. 2010. Randomization tests for distinguishing social influence and homophily effects. In Proc. of the International Conference on World Wide Web (WWW’10). ACM, 601--610.

Digital Library

[21]

L. C. Freeman. 1977. A set of measures of centrality based on betweenness. Sociometry 40 (1977), 35--41.

[22]

J. H. Friedman. 2001. Greedy function approximation: A gradient boosting machine. Ann. Stat. (2001), 1189--1232.

[23]

F. Garcin, B. Faltings, and R. Jurca. 2009. Aggregating reputation feedback. In Proc. of the 1st International Conference on Reputation: Theory and Technology. Springer, 62--74.

[24]

J. Golbeck. 2006. Generating predictive movie recommendations from trust in social networks. In Proc. of the International Conference on Trust Management (iTrust’06), 93--104.

Digital Library

[25]

J. Golbeck and J. Hendler. 2006. Inferring binary trust relationships in web-based social networks. ACM Trans. Internet Technol. 6, 4 (2006), 497--529.

Digital Library

[26]

N. Gong, A. Talwalkar, L. Mackey, L. Huang, E. Shin, E. Stefanov, E. Shi, and D. Song. 2014. Joint link prediction and attribute inference using a social-attribute network. ACM Trans. Intell. Syst. Technol. 5, 2 (2014), 27.

Digital Library

[27]

P. Good. 2006. Permutation, Parametric, and Bootstrap Tests of Hypotheses. Springer Science 8 Business Media.

Digital Library

[28]

F. Hendrikx, K. Bubendorfer, and R. Chard. 2015. Reputation systems: A survey and taxonomy. J. Parallel Distrib. Comput. 75 (2015), 184--197.

Digital Library

[29]

T. Hogg and L. Adamic. 2004. Enhancing reputation mechanisms via online social networks. In Proc. of the ACM Conference on Electronic Commerce (EC’04). ACM, New York, 236--237.

Digital Library

[30]

A. Jøsang, R. Ismail, and C. Boyd. 2007. A survey of trust and reputation systems for online service provision. Decision Supp. Syst. 43, 2 (2007), 618--644.

Digital Library

[31]

S.D. Kamvar, M. Schlosser, and H. Garcia-Molina. 2003. The EigenTrust algorithm for reputation management in P2P networks. In Proc. of the International Conference on World Wide Web (WWW’03). ACM, 640--651.

Digital Library

[32]

S. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. 2006. Automatically assessing review helpfulness. In Proc. of the International Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 423--430.

Digital Library

[33]

R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. 2004. Structure and evolution of blogspace. Commun. ACM 47, 12 (2004), 35--39.

Digital Library

[34]

J. Leskovec, D. Huttenlocher, and J. Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proc. of the International Conference on World Wide Web (WWW’10), 641--650.

Digital Library

[35]

R. Levien. 2009. Attack-resistant trust metrics. In Computing with Social Trust. Springer, 121--132.

[36]

Y. Liu, X. Huang, A. An, and X. Yu. 2008. Modeling and predicting the helpfulness of online reviews. In Proc. of the International Conference on Data Mining (ICDM’08). IEEE, 443--452.

Digital Library

[37]

Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. 2010. Exploiting social context for review quality prediction. In Proc. of the International Conference on World Wide Web (WWW’10). ACM, 691--700.

Digital Library

[38]

H. Ma, I. King, and M. Lyu. 2009. Learning to recommend with social trust ensemble. In Proc. of the International Conference on Research and Development in Information Retrieval (SIGIR’09). ACM, ACM Press, 203--210.

Digital Library

[39]

J. McAlexander, J. W. Schouten, and H. F. Koenig. 2002. Building brand community. J. Market. 66, 1 (2002), 38--54.

[40]

M. Melnik and J. Alm. 2002. Does a seller’s ecommerce reputation matter? Evidence from eBay auctions. J. Industr. Econ. 50, 3 (2002), 337--349.

[41]

A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. 2007. Measurement and analysis of online social networks. In Proc. of the ACM SIGCOMM Conference on Internet Measurement. ACM, 29--42.

Digital Library

[42]

K. Musial and P. Kazienko. 2013. Social networks on the internet. World Wide Web 16, 1 (2013), 31--72.

[43]

J. Neville, O. Şimşek, D. Jensen, J. Komoroske, K. Palmer, and H. Goldberg. 2005. Using relational knowledge discovery to prevent securities fraud. In Proc. of the ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (SIGKDD’05). ACM Press, 449--458.

Digital Library

[44]

M. Newman. 2010. Networks: An Introduction. Oxford University Press.

[45]

M. O’Mahony and B. Smyth. 2009. Learning to recommend helpful hotel reviews. In Proc. of the International Conference on Recommender Systems (ACM RecSys’09). ACM, New York, NY, 305--308.

Digital Library

[46]

J. Pujol, Ramon R. Sangüesa, and J. Delgado. 2002. Extracting reputation in multi agent systems by means of social network topology. In Proc. of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS’02). ACM, 467--474.

Digital Library

[47]

M. Richardson, R. Agrawal, and P. Domingos. 2003. Trust management for the semantic web. In Proc. of the International Conference on Semantic Web (ISWC’03). Springer, 351--368.

Digital Library

[48]

J. Ronqui and G. Travieso. 2015. Analyzing complex networks through correlations in centrality measurements. J. Stat. Mech.: Theory Exper. 2015, 5 (2015), P05030.

[49]

M. Sirivianos, K. Kim, J.W. Gan, and X. Yang. 2014. Leveraging social feedback to verify online identity claims. ACM Trans. Web 8, 2 (2014), 9.

Digital Library

[50]

J. Tang, X. Hu, H. Gao, and H. Liu. 2013. Exploiting local and global social context for recommendation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI 2013), 2712--2718.

Digital Library

[51]

S. Wasserman and K. Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press, New York, NY.

[52]

J. Weng, E. Lim, Jing J. Jiang, and Q. He. 2010. Twitterrank: Finding topic-sensitive influential twitterers. In Proc. of the ACM International Conference on Web Search and Data Mining (WSDM’10). ACM, New York, NY, 261--270.

Digital Library

[53]

J. Yang, C. Hauff, A. Bozzon, and G. J. Houben. 2014. Asking the right question in collaborative Q8A systems. In Proc. of the ACM Conference on Hypertext and Social Media (HT’14). ACM, 179--189.

Digital Library

[54]

Z. Yin, M. Gupta, T. Weninger, and J. Han. 2010. LINKREC: A unified framework for link recommendation with user attributes and graph structure. In Proc. of the International Conference on World Wide Web (WWW’10). ACM, 1211--1212.

Digital Library

[55]

R. Zhang and Y. Mao. 2014. Trust prediction via belief propagation. ACM Trans. Inform. Syst. 32, 3 (2014), 1--27.

Digital Library

[56]

Z. Zhang and B. Varadarajan. 2006. Utility scoring of product reviews. In Proc. of the ACM Conference on Information and Knowledge Management (CIKM’06). ACM Press, 51--57.

Digital Library

[57]

Y. Zhu. 2010. Measurement and analysis of an online content voting network: A case study of digg. In Proc. of the International Conference on World Wide Web (WWW’10). ACM, 1039--1048.

Digital Library

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Index Terms

Using Centrality Measures to Predict Helpfulness-Based Reputation in Trust Networks
1. Human-centered computing
  1. Collaborative and social computing
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Reputation systems
      2. Social networking sites

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 17, Issue 1

Special Issue on Affect and Interaction in Agent-based Systems and Social Media and Regular Paper

February 2017

213 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3036639

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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Copyright © 2017 ACM.

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Publication History

Published: 25 February 2017

Accepted: 01 July 2016

Revised: 01 April 2016

Received: 01 December 2015

Published in TOIT Volume 17, Issue 1

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project BA2Kno (Business Analytics to Know)
Distretto Tecnologico CyberSecurity
Program “Programma Operativo Nazionale Ricerca e Competitivitá” 2007-2013
Italian Ministry of Education
University and Research
“Laboratorio in Rete di Service Innovation”

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https://doi.org/10.1680/jmapl.21.00025
Ho KHou YGeorgiades MFong K(2024)Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network AnalysisIEEE Access10.1109/ACCESS.2024.339772312(65058-65077)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3397723
Kaur MKumar Verma V(2024)Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of thingsJournal of Network and Computer Applications10.1016/j.jnca.2024.103872226:COnline publication date: 1-Jun-2024
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