Measurement of Online Discussion Authenticity within Online Social Media

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 classif...

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Bibliographic Details
Published in:2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) pp. 627 - 629
Main Authors: Elyashar, Aviad, Bendahan, Jorge, Puzis, Rami, Sanmateu, Maria-Amparo
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 31.07.2017
Series:ACM Conferences
Subjects:
Data mining
Estimation
Feature extraction
Human-centered computing
Human-centered computing > Collaborative and social computing
Human-centered computing > Collaborative and social computing > Collaborative and social computing systems and tools
Human-centered computing > Collaborative and social computing > Collaborative and social computing systems and tools > Social networking sites
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social media
Human-centered computing > Human computer interaction (HCI)
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Web-based interaction
Information systems
Information systems > World Wide Web
Information systems > World Wide Web > Web applications
Information systems > World Wide Web > Web applications > Social networks
Labeling
Networks
Networks > Network types
Networks > Network types > Overlay and other logical network structures
Networks > Network types > Overlay and other logical network structures > Social media networks
Training
Twitter
ISBN:1450349935, 9781450349932
ISSN:2473-991X
Online Access:Get full text
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Summary: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.
ISBN:1450349935
9781450349932
ISSN:2473-991X
DOI:10.1145/3110025.3110115