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
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
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 - http://www.scopus.com/inward/record.url?scp=85040247059&partnerID=8YFLogxK
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
EP - 629
BT - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
A2 - Diesner, Jana
A2 - Ferrari, Elena
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
Y2 - 31 July 2017 through 3 August 2017
ER -