research-article

Reputation Mechanisms in on-line Social Networks: The case of an Influence Estimation System in Twitter

Authors:

G. Kanellopoulos,

A. TsalgatidouAuthors Info & Claims

SEEDA-CECNSM '16: Proceedings of the SouthEast European Design Automation, Computer Engineering, Computer Networks and Social Media Conference

Pages 98 - 105

https://doi.org/10.1145/2984393.2984400

Published: 25 September 2016 Publication History

Abstract

Online social networks have changed the way people communicate, create content and share it via online social actions. In order for users to engage in a social network application, they need mechanisms that will help them to find other users that they can trust in some context and also qualitative and useful content. Reputation systems offer such mechanisms and are thus essential in Social Networks (SNs). Due to their open and social nature and to their different objectives, reputation systems for SN-based applications comprise of various different mechanisms which fulfill the different objectives in the specific application context. There is thus the need for a systematic study of the specific aspects of these systems, which can provide insights for the design of reputation systems in specific application contexts. In our paper we present a taxonomy of reputation systems for various SN applications based on their specific aspects. We then use this taxonomy to design a reputation system for microblogging systems and present our implementation for a reputation system for Twitter which estimates a specific notion of reputation; the influence of users and of specific subjects of discussion.

References

[1]

Marti, S., and Garcia-Molina, H. 2006. Taxonomy of trust: categorizing P2P reputation systems. Comput. Netw. 50, 4 (March 2006), 472--484.

Digital Library

[2]

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

Digital Library

[3]

Khalid, O., Khan, S. U., Madani, S. A., Hayat, K., Khan, M. I., Min-Allah, N., Kolodziej, J., Wang, L., Zeadally, S. Chen, D. 2013. Comparative Study of Trust and Reputation Systems for Wireless Sensor Networks. Secur. Commun. Netw. 6, 6, 669--688.

[4]

YouTube, www.youtube.com {last accessed on 30/6/2016}

[5]

Flickr, www.flickr.com {last accessed on 30/6/2016}

[6]

Techcrunch, www.techcrunch.com {last accessed on 30/6/2016}

[7]

Mashable, www.mashable.com {last accessed on 30/6/2016}

[8]

Twitter, www.twitter.com {last accessed on 30/6/2016}

[9]

Yelp, www.yelp.com {last accessed on 30/6/2016}

[10]

Epinions, www.epinions.com {last accessed on 30/6/2016}

[11]

Facebook, www.facebook.com {last accessed on 30/6/2016}

[12]

Ahmed, S. and Ezeife, C. I. Discovering Influential Nodes from Trust Network. In Proceedings of the 28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013). SAC '13. ACM, New York, NY, USA, 121--128.

Digital Library

[13]

Agarwal, N, Liu, H., Tang, L. and Yu, P. S. Identifying the Influential Bloggers in a Community. In Proceedings of the International Conference on Web Search and Web Data Mining (2008). (Palo Alto, California, USA, Feb. 11-12, 2008). WSDM '08. ACM, New York, NY, 207--218.

Digital Library

[14]

Reddit, www.reddit.com {last accessed on 30/6/2016}

[15]

Dellarocas, C. 2010. Online Reputation Systems: How to Design One That Does What You Need. MIT Sloan Management Review, 51, 3 (Issue of Summer 2010), 33--38.

[16]

Farmer, R. and Glass, B. 2010. Building Web Reputation Systems (1st ed.). Yahoo! Press, USA.

Digital Library

[17]

Nickerson, R., Muntermann, J., Varshney, U, and Isaac, H. Taxonomy Development in Information Systems: Developing a Taxonomy of Mobile Applications. In Proceedings of the European Conference on Information Systems (2009). ECIS 2009 Proceedings, Paper 388.

[18]

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

Digital Library

[19]

Digg, www.digg.com {last accessed on 30/6/2016}

[20]

Delicious, www.delicious.com {last accessed on 30/6/2016}

[21]

Han, Y., Kim, L., and Cha, J. Evaluation of User Reputation on YouTube. In Proceedings of the 3d International Conference on Online Communities and Social Computing (Jul. 19-24, 2009). OCSC '09. LNCS, 346--353.

Digital Library

[22]

TrackMaven retweet report, http://trackmaven.com/resources/retweet-report/ {last accessed on 30/6/2016}

[23]

Wikipedia, www.wikipedia.com {last accessed on 30/6/2016}

[24]

Klout, http://klout.com/ {last accessed on 30/6/2016}

[25]

Amazon, www.amazon.com {last accessed on 30/6/2016}

[26]

LinkedIn, www.linkedin.com {last accessed on 30/6/2016}

[27]

Twitter widgets, https://dev.twitter.com/web/overview {last accessed on 30/6/2016}

[28]

https://www.javascript.com/ {last accessed on 30/6/2016}

[29]

https://jquery.com/ {last accessed on 30/6/2016}

[30]

http://www.css3.info/modules/ {last accessed on 30/6/2016}

[31]

https://www.w3.org/TR/html5/ {last accessed on 30/6/2016}

Cited By

Katikaridi MTsalgatidou AKoutrouli E(2021)Delurking and Influence Maximization in Online Social Networks2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)10.1109/SEEDA-CECNSM53056.2021.9566208(1-6)Online publication date: 24-Sep-2021
https://doi.org/10.1109/SEEDA-CECNSM53056.2021.9566208
Al-Yazidi SBerri JAl-Qurishi MAl-Alrubaian M(2020)Measuring Reputation and Influence in Online Social Networks: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2020.2999033(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2999033
Koutrouli EDaskalakis CTsalgatidou A(2018)Finding Topic-Specific Trends and Influential Users in Social NetworksDiscovery Science10.1007/978-3-030-01771-2_26(405-420)Online publication date: 7-Oct-2018
https://doi.org/10.1007/978-3-030-01771-2_26

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cover image ACM Other conferences

SEEDA-CECNSM '16: Proceedings of the SouthEast European Design Automation, Computer Engineering, Computer Networks and Social Media Conference

September 2016

126 pages

ISBN:9781450348102

DOI:10.1145/2984393

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 September 2016

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SEEDA-CECNSM '16

SEEDA-CECNSM '16: SouthEast European Design Automation, Computer Engineering, Computer Networks and Social Media Conference

September 25 - 27, 2016

Kastoria, Greece

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Cited By

Katikaridi MTsalgatidou AKoutrouli E(2021)Delurking and Influence Maximization in Online Social Networks2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)10.1109/SEEDA-CECNSM53056.2021.9566208(1-6)Online publication date: 24-Sep-2021
https://doi.org/10.1109/SEEDA-CECNSM53056.2021.9566208
Al-Yazidi SBerri JAl-Qurishi MAl-Alrubaian M(2020)Measuring Reputation and Influence in Online Social Networks: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2020.2999033(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2999033
Koutrouli EDaskalakis CTsalgatidou A(2018)Finding Topic-Specific Trends and Influential Users in Social NetworksDiscovery Science10.1007/978-3-030-01771-2_26(405-420)Online publication date: 7-Oct-2018
https://doi.org/10.1007/978-3-030-01771-2_26

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