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Bot detection in twitter landscape using unsupervised learning

Authors:

Ahmed Anwar,

Ussama YaqubAuthors Info & Claims

dg.o '20: Proceedings of the 21st Annual International Conference on Digital Government Research

Pages 329 - 330

https://doi.org/10.1145/3396956.3401801

Published: 16 June 2020 Publication History

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Abstract

The aim of this paper is to identify and understand bot activity in twitter discussion. The prevalence of Twitter bots have gained significant limelight recently due to their misuse in influencing public sentiment for political gains. For our analysis, we use Twitter data of 2019 Canadian Elections. We perform principal component analysis and K-means clustering on the data set. Using the results we isolate bots from human accounts.

References

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Zhouhan Chen, Rima S Tanash, Richard Stoll, and Devika Subramanian. 2017. Hunting malicious bots on twitter: An unsupervised approach. In International Conference on Social Informatics. Springer, 501–510.

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Peng HZhang JHuang XHao ZLi AYu ZYu P(2024)Unsupervised Social Bot Detection via Structural Information TheoryACM Transactions on Information Systems10.1145/366052242:6(1-42)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660522
Chanti SChithralekha T(2023)RGF-Bot: A Novel Feature Selection Method to Identify Malicious Bot Accounts on Social Networking Sites Using Machine LearningSN Computer Science10.1007/s42979-023-02263-54:6Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1007/s42979-023-02263-5
Aljabri MZagrouba RShaahid AAlnasser FSaleh AAlomari D(2023)Machine learning-based social media bot detection: a comprehensive literature reviewSocial Network Analysis and Mining10.1007/s13278-022-01020-513:1Online publication date: 5-Jan-2023
https://doi.org/10.1007/s13278-022-01020-5
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dg.o '20: Proceedings of the 21st Annual International Conference on Digital Government Research

June 2020

389 pages

ISBN:9781450387910

DOI:10.1145/3396956

Editors:
Seok-Jin Eom,
Jooho Lee

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 16 June 2020

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dg.o '20

dg.o '20: The 21st Annual International Conference on Digital Government Research

June 15 - 19, 2020

Seoul, Republic of Korea

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Peng HZhang JHuang XHao ZLi AYu ZYu P(2024)Unsupervised Social Bot Detection via Structural Information TheoryACM Transactions on Information Systems10.1145/366052242:6(1-42)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1145/3660522
Chanti SChithralekha T(2023)RGF-Bot: A Novel Feature Selection Method to Identify Malicious Bot Accounts on Social Networking Sites Using Machine LearningSN Computer Science10.1007/s42979-023-02263-54:6Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1007/s42979-023-02263-5
Aljabri MZagrouba RShaahid AAlnasser FSaleh AAlomari D(2023)Machine learning-based social media bot detection: a comprehensive literature reviewSocial Network Analysis and Mining10.1007/s13278-022-01020-513:1Online publication date: 5-Jan-2023
https://doi.org/10.1007/s13278-022-01020-5
Chawla VKapoor Y(2023)A hybrid framework for bot detection on twitter: Fusing digital DNA with BERTMultimedia Tools and Applications10.1007/s11042-023-14730-582:20(30831-30854)Online publication date: 1-Mar-2023
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Shukla RSinha AChaudhary A(2022)TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social NetworksElectronics10.3390/electronics1105074311:5(743)Online publication date: 28-Feb-2022
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Kausar STahir BMehmood M(2022)Push-to-Trend: A Novel Framework to Detect Trend Promoters in Trending HashtagsIEEE Access10.1109/ACCESS.2022.321689110(113005-113017)Online publication date: 2022
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Yilmaz GBilen M(2022)Sensemaking in a Networked World: COVID-19 Vaccine Hesitancy in TurkeyCommunication Studies10.1080/10510974.2022.209728573:4(347-363)Online publication date: 7-Jul-2022
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https://doi.org/10.3233/IP-210321
Guimarães NFigueira ÁTorgo L(2021)Towards a pragmatic detection of unreliable accounts on social networksOnline Social Networks and Media10.1016/j.osnem.2021.10015224(100152)Online publication date: Jul-2021
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Di Pietro RRaponi SCaprolu MCresci SDi Pietro RRaponi SCaprolu MCresci S(2020)Information DisorderNew Dimensions of Information Warfare10.1007/978-3-030-60618-3_2(7-64)Online publication date: 4-Dec-2020
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