research-article

Measurement of Online Discussion Authenticity within Online Social Media

Authors:

Aviad Elyashar,

Jorge Bendahan,

Rami Puzis,

Maria-Amparo SanmateuAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 627 - 629

https://doi.org/10.1145/3110025.3110115

Published: 31 July 2017 Publication History

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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.

References

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G. Wang, C. Wilson, X. Zhao, Y. Zhu, M. Mohanlal, H. Zheng, and B. Y. Zhao, "Serf and turf: crowdturfing for fun and profit," in Proceedings of the 21st international conference on World Wide Web.

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K. Lee, P. Tamilarasan, and J. Caverlee, "Crowdturfers, campaigns, and social media: Tracking and revealing crowdsourced manipulation of social media." in ICWSM, 2013.

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[4]

A. Elyashar, J. Bendahan, and R. Puzis, "Is the online discussion manipulated? quantifying the online discussion authenticity within online social media," arXiv preprint arXiv:1708.02763, 2017.

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Agustina CPurwanto PFarikhin F(2024)Enhancing Sentiment Analysis Accuracy in Borobudur Temple Visitor Reviews through Semi-Supervised Learning and SMOTE UpsamplingJournal of Advances in Information Technology10.12720/jait.15.4.492-49915:4(492-499)Online publication date: 2024
https://doi.org/10.12720/jait.15.4.492-499
El Mendili FFattah MBerros NFilaly YEl Bouzekri El Idrissi Y(2024)Enhancing detection of malicious profiles and spam tweets with an automated honeypot framework powered by deep learningInternational Journal of Information Security10.1007/s10207-023-00796-723:2(1359-1388)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10207-023-00796-7
Elyashar AReuben MShabtai APuzis R(2023)Social Network Analysis for Disinformation DetectionMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_30(681-701)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_30
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Measurement of Online Discussion Authenticity within Online Social Media
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
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  2. Information systems applications

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Published In

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 July 2017

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ASONAM '17

Sponsor:

SIGKDD

ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

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Agustina CPurwanto PFarikhin F(2024)Enhancing Sentiment Analysis Accuracy in Borobudur Temple Visitor Reviews through Semi-Supervised Learning and SMOTE UpsamplingJournal of Advances in Information Technology10.12720/jait.15.4.492-49915:4(492-499)Online publication date: 2024
https://doi.org/10.12720/jait.15.4.492-499
El Mendili FFattah MBerros NFilaly YEl Bouzekri El Idrissi Y(2024)Enhancing detection of malicious profiles and spam tweets with an automated honeypot framework powered by deep learningInternational Journal of Information Security10.1007/s10207-023-00796-723:2(1359-1388)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10207-023-00796-7
Elyashar AReuben MShabtai APuzis R(2023)Social Network Analysis for Disinformation DetectionMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_30(681-701)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_30
Wu Y(2022)Distinguishing the binary of news – fake and real: The illusory truth effectJournal of Applied Journalism & Media Studies10.1386/ajms_00042_111:3(287-308)Online publication date: 1-Oct-2022
https://doi.org/10.1386/ajms_00042_1
Elyashar ABendahan JPuzis R(2022)Detecting Clickbait in Online Social Media: You Won’t Believe How We Did ItCyber Security, Cryptology, and Machine Learning10.1007/978-3-031-07689-3_28(377-387)Online publication date: 23-Jun-2022
https://doi.org/10.1007/978-3-031-07689-3_28
Monakhov S(2021)How analysis of mobile app reviews problematises linguistic approaches to internet troll detectionHumanities and Social Sciences Communications10.1057/s41599-021-00968-78:1Online publication date: 18-Nov-2021
https://doi.org/10.1057/s41599-021-00968-7
Monakhov S(2020)Understanding Troll Writing as a Linguistic PhenomenonIntelligent Systems and Applications10.1007/978-3-030-55187-2_26(315-334)Online publication date: 25-Aug-2020
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Di Pietro RCaprolu MRaponi S(2020)Next Generation Information Warfare: Rationales, Scenarios, Threats, and Open IssuesInformation Systems Security and Privacy10.1007/978-3-030-49443-8_2(24-47)Online publication date: 28-Jun-2020
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Zannettou SCaulfield TDe Cristofaro ESirivianos MStringhini GBlackburn J(2019)Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the WebCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316495(218-226)Online publication date: 13-May-2019
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Meinert JMirbabaie MDungs SAker A(2018)Is It Really Fake? – Towards an Understanding of Fake News in Social Media CommunicationSocial Computing and Social Media. User Experience and Behavior10.1007/978-3-319-91521-0_35(484-497)Online publication date: 31-May-2018
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