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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