Measurement of online discussion authenticity within online social media

Aviad Elyashar; Jorge Bendahan; Rami Puzis; Maria Amparo Sanmateu

doi:10.1145/3110025.3110115

Measurement of online discussion authenticity within online social media

Aviad Elyashar
, Jorge Bendahan
, Rami Puzis
, Maria Amparo Sanmateu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this paper, we propose an approach for estimating the authenticity of online discussions based on the similarity of online social media (OSM) accounts participating in the online discussion to known abusers and legitimate accounts. Our method uses similarity functions for the analysis and classification of OSM accounts. The proposed methods are demonstrated using Twitter data collected for this study and a previously published Arabic Honeypot dataset. The data collected during this study includes manually labeled accounts and a ground truth collection of abusers from crowdturfing platforms. Demonstration of the discussion topic’s authenticity, derived from account similarity functions, shows that the suggested approach is effective for discriminating between topics that were strongly promoted by abusers and topics that attracted authentic public interest.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Editors	Jana Diesner, Elena Ferrari, Guandong Xu
Publisher	Association for Computing Machinery, Inc
Pages	627-629
Number of pages	3
ISBN (Electronic)	9781450349932
DOIs	https://doi.org/10.1145/3110025.3110115
State	Published - 31 Jul 2017
Event	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia Duration: 31 Jul 2017 → 3 Aug 2017

Publication series

Name	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

Conference

Conference	9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Country/Territory	Australia
City	Sydney
Period	31/07/17 → 3/08/17

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1145/3110025.3110115

Cite this

Elyashar, A., Bendahan, J., Puzis, R., & Sanmateu, M. A. (2017). Measurement of online discussion authenticity within online social media. In J. Diesner, E. Ferrari, & G. Xu (Eds.), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 (pp. 627-629). (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3110025.3110115

Elyashar, Aviad ; Bendahan, Jorge ; Puzis, Rami et al. / Measurement of online discussion authenticity within online social media. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. editor / Jana Diesner ; Elena Ferrari ; Guandong Xu. Association for Computing Machinery, Inc, 2017. pp. 627-629 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

@inproceedings{3347018ba831438590c7a3c2bb37d73e,

title = "Measurement of online discussion authenticity within online social media",

abstract = "In this paper, we propose an approach for estimating the authenticity of online discussions based on the similarity of online social media (OSM) accounts participating in the online discussion to known abusers and legitimate accounts. Our method uses similarity functions for the analysis and classification of OSM accounts. The proposed methods are demonstrated using Twitter data collected for this study and a previously published Arabic Honeypot dataset. The data collected during this study includes manually labeled accounts and a ground truth collection of abusers from crowdturfing platforms. Demonstration of the discussion topic{\textquoteright}s authenticity, derived from account similarity functions, shows that the suggested approach is effective for discriminating between topics that were strongly promoted by abusers and topics that attracted authentic public interest.",

author = "Aviad Elyashar and Jorge Bendahan and Rami Puzis and Sanmateu, \{Maria Amparo\}",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 ; Conference date: 31-07-2017 Through 03-08-2017",

year = "2017",

month = jul,

day = "31",

doi = "10.1145/3110025.3110115",

language = "English",

series = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",

publisher = "Association for Computing Machinery, Inc",

pages = "627--629",

editor = "Jana Diesner and Elena Ferrari and Guandong Xu",

booktitle = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",

}

Elyashar, A, Bendahan, J, Puzis, R & Sanmateu, MA 2017, Measurement of online discussion authenticity within online social media. in J Diesner, E Ferrari & G Xu (eds), Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Association for Computing Machinery, Inc, pp. 627-629, 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, Sydney, Australia, 31/07/17. https://doi.org/10.1145/3110025.3110115

Measurement of online discussion authenticity within online social media. / Elyashar, Aviad; Bendahan, Jorge; Puzis, Rami et al.
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. ed. / Jana Diesner; Elena Ferrari; Guandong Xu. Association for Computing Machinery, Inc, 2017. p. 627-629 (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Measurement of online discussion authenticity within online social media

AU - Elyashar, Aviad

AU - Bendahan, Jorge

AU - Puzis, Rami

AU - Sanmateu, Maria Amparo

PY - 2017/7/31

Y1 - 2017/7/31

N2 - In this paper, we propose an approach for estimating the authenticity of online discussions based on the similarity of online social media (OSM) accounts participating in the online discussion to known abusers and legitimate accounts. Our method uses similarity functions for the analysis and classification of OSM accounts. The proposed methods are demonstrated using Twitter data collected for this study and a previously published Arabic Honeypot dataset. The data collected during this study includes manually labeled accounts and a ground truth collection of abusers from crowdturfing platforms. Demonstration of the discussion topic’s authenticity, derived from account similarity functions, shows that the suggested approach is effective for discriminating between topics that were strongly promoted by abusers and topics that attracted authentic public interest.

AB - In this paper, we propose an approach for estimating the authenticity of online discussions based on the similarity of online social media (OSM) accounts participating in the online discussion to known abusers and legitimate accounts. Our method uses similarity functions for the analysis and classification of OSM accounts. The proposed methods are demonstrated using Twitter data collected for this study and a previously published Arabic Honeypot dataset. The data collected during this study includes manually labeled accounts and a ground truth collection of abusers from crowdturfing platforms. Demonstration of the discussion topic’s authenticity, derived from account similarity functions, shows that the suggested approach is effective for discriminating between topics that were strongly promoted by abusers and topics that attracted authentic public interest.

UR - https://www.scopus.com/pages/publications/85040247059

U2 - 10.1145/3110025.3110115

DO - 10.1145/3110025.3110115

M3 - Conference contribution

AN - SCOPUS:85040247059

T3 - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

SP - 627

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BT - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

A2 - Diesner, Jana

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A2 - Xu, Guandong

PB - Association for Computing Machinery, Inc

T2 - 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

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Elyashar A, Bendahan J, Puzis R, Sanmateu MA. Measurement of online discussion authenticity within online social media. In Diesner J, Ferrari E, Xu G, editors, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017. Association for Computing Machinery, Inc. 2017. p. 627-629. (Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017). doi: 10.1145/3110025.3110115

Measurement of online discussion authenticity within online social media

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