An evolution-based robust social influence evaluation method in Online Social Networks

Zhu, Feng, Liu, Guanfeng, Liu, An, Zhao, Lei and Zhou, Xiaofang (2014). An evolution-based robust social influence evaluation method in Online Social Networks. In: Boualem Benatallah, Azer Bestavros, Yannis Manolopoulos, Athena Vakali and Yanchun Zhang, Web Information Systems Engineering - WISE 2014: Proceedings Part 2. 15th International Conference on Web Information Systems Engineering (WISE 2014), Thessaloniki, Greece, (141-157). 12-14 October 2014. doi:10.1007/978-3-319-11746-1_10


Author Zhu, Feng
Liu, Guanfeng
Liu, An
Zhao, Lei
Zhou, Xiaofang
Title of paper An evolution-based robust social influence evaluation method in Online Social Networks
Conference name 15th International Conference on Web Information Systems Engineering (WISE 2014)
Conference location Thessaloniki, Greece
Conference dates 12-14 October 2014
Proceedings title Web Information Systems Engineering - WISE 2014: Proceedings Part 2   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-11746-1_10
Open Access Status
ISBN 9783319117454
9783319117461
ISSN 0302-9743
1611-3349
Editor Boualem Benatallah
Azer Bestavros
Yannis Manolopoulos
Athena Vakali
Yanchun Zhang
Volume 8787
Start page 141
End page 157
Total pages 17
Collection year 2015
Language eng
Abstract/Summary Online Social Networks (OSNs) are becoming popular and attracting lots of participants. In OSN based e-commerce platforms, a buyer’s review of a product is one of the most important factors for other buyers’ decision makings. A buyer who provides high quality reviews thus has strong social influence, and can impact a large number of participants’ purchase behaviours in OSNs. However, the dishonest participants can cheat the existing social influence evaluation models by using some typical attacks, like Constant and Camouflage, to obtain fake strong social influence. Therefore, it is significant to accurately evaluate such social influence to recommend the participants who have strong social influences and provide high quality product reviews. In this paper, we propose an Evolutionary-Based Robust Social Influence (EB-RSI) method based on the trust evolutionary models. In our EB-RSI, we propose four influence impact factors in social influence evaluation, i.e., Total Trustworthiness (TT), Fluctuant Trend of Being Advisor (FTBA), Fluctuant Trend of Trustworthiness (FTT) and Trustworthiness Area (TA). They are all significant in the influence evaluation. We conduct experiments onto a real social network dataset Epinions, and validate the effectiveness and robustness of our EB-RSI by comparing with state-of-the-art method, SoCap. The experimental results demonstrate that our EB-RSI can more accurately evaluate participants’ social influence than SoCap.
Keyword Election prediction
Event detection
Sentiment analysis
Micro-blogs
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 16 Apr 2015, 12:44:23 EST by Caitlin Maskell on behalf of School of Information Technol and Elec Engineering