Author details

All percentages presented in this section are calculated by averaging the differences on the rate of successfully influenced social nodes between two or more algorithms where the GADM algorithm will serve as the benchmark. The analysis showed that by comparing results generated between GADM (base algorithm without trusted social node) and T-GADM (enhanced algorithm with trusted social node), the T-GADM algorithm yields 5.79% increment on the rate of successfully influenced social nodes compared to GADM. Such increment on the rate of successfully influenced social nodes is because social nodes that are trustworthy have higher tendency of being accepted by other social nodes; therefore, influence spread by these trustworthy social nodes may strongly be accepted. Furthering the analysis, results generated show that the

Figure 6. Acceptance probability for GADM vs. T-GADM vs. DST-GADM Tier 1, 2 and 3.

16 Recent Progress in Parallel and Distributed Computing

Figure 7. Acceptance rates for GADM vs. T-GADM vs. DST-GADM Tier 1, 2 and 3.

Yap Hock Yeow and Lim Tong-Ming\*

\*Address all correspondence to: tongmingl@sunway.edu.my

Faculty of Science and Technology, Sunway University, Petaling Jaya, Malaysia

## References


References

2015].

Island, United States, 2013.

18 Recent Progress in Parallel and Distributed Computing

United States, 2011.

no. 1, pp. 95–112, 2010.

1, pp. 468–494, 2010.

2, pp. 127–157, 2002.

2013.

Insider, United Kingdom, 2013.

[1] I. T. Union, "Internet Users (Per 100 People) 1981–2014," The World Bank, December 2014. [Online]. Available: http://data.worldbank.org/indicator/IT.NET.USER.P2. [Accessed 2 July

[2] A. Gesenhues, "Survey: 90% Of Customers Say Buying Decisions Are Influenced By

[3] H. Paquette, "Social Media as a Marketing Tool: A Literature Review," University of Rhode

[4] K. Quesenberry, "Social Media Is Too Important to Be Left to the Marketing Depart-

[5] M. Ewing, "71% More Likely to Purchase Based on Social Media Referrals," HubSpot,

[6] A. Josang, R. Ismail and C. Byod, "A Survey of Trust and Reputation Systems for Online

[7] J. Caverlee, L. Liu and S. Webb, "The SocialTrust framework for trusted social information management: Architecture and algorithms," Journal of Information Science, vol. 180,

[8] E. Hargittai, L. Fullerton, E. Menchen-Trevino and K. Thomas, "Trust online: young adults' evaluation of web content," International Journal of Communication, vol. 4, no.

[9] P. Resnick and R. Zeckhauser, "Trust among strangers in internet transactions: empirical analysis of ebay's reputation system," Advances in Applied Microeconomics, vol. 11, no.

[10] S. Hendrickson, "Case Study: How Content Diffuses Through Different Social Net-

[11] H. Leonard, "The Fascinating Spread of Content Through Social Networks," Business

[12] J. Tierney, "Social Media Helps Police, Cities Spread Information," Daily Herald, Utah,

[13] B. Marcus, F. Machilek and A. Schutz, "Personality in cyberspace: personal Web sites as media for personality expressions and impressions," Journal of Personality and Social

[14] A. Renninger and W. Shumar, Building Virtual Communities: Learning and Change in

[15] R. Morgan and S. Hunt, "The commitment-trust theory of relationship marketing," Jour-

Cyberspace, United Kingdom: Cambridge University Press, 2002.

Online Reviews," Marketing Land, United States, 2013.

ment," Harvard Business Publishing, United States, 2016.

works," Social Media Today, United States, 2013.

Psychology, vol. 90, no. 6, pp. 1014–1031, 2006.

nal of Marketing, vol. 58, no. 1, pp. 20–38, 1994.

Service Provision," in Decision Support Systems, Amsterdam, 2007.

