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Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

Authors:

Erdem Beğenilmiş,

Suzan UskudarliAuthors Info & Claims

WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

Article No.: 36, Pages 1 - 9

https://doi.org/10.1145/3227609.3227665

Published: 25 June 2018 Publication History

Abstract

There is an increasing incidence in negative propaganda and fake news, which has recently gained lots of attention during the 2016 elections in United States, France, and United Kingdom. Bots and hired users collaborate to make messages seen and persist so they may spread and gain support. Assuming that most Twitter users post without predetermined, malicious intent, there is a need for automated detection of organized behavior to protect users from manipulation. This work proposes an automated approach to classify tweets with organized behavior. Supervised learning methods are used to classify the tweets by using a training data set with 850 records based on the analysis of over 200 million tweets. Our model gave promising results for detection of organized behavior and this motivated us to proceed with the generation of two more classifiers such as ["political", "non-political"] and ["pro-Trump", "pro-Hillary","neither"]. In each cases, the random forest algorithm consistently results in high scores with an average accuracy and f-measure above 0.95.

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Cited By

Moghaddam SAbbaspour M(2023)Friendship Preference: Scalable and Robust Category of Features for Social Bot DetectionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315900720:2(1516-1528)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TDSC.2022.3159007
Ellaky ZBenabbou FOuahabi S(2023)Systematic Literature Review of Social Media Bots Detection SystemsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.04.00435:5(101551)Online publication date: May-2023
https://doi.org/10.1016/j.jksuci.2023.04.004
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
Show More Cited By

Index Terms

Organized Behavior Classification of Tweet Sets using Supervised Learning Methods
1. Computer systems organization
  1. Real-time systems
2. Information systems
  1. Information systems applications
    1. Decision support systems
  2. World Wide Web
    1. Web applications
      1. Social networks

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cover image ACM Other conferences

WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

June 2018

398 pages

ISBN:9781450354899

DOI:10.1145/3227609

Copyright © 2018 ACM.

© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2018

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WIMS '18

WIMS '18: 8th International Conference on Web Intelligence, Mining and Semantics

June 25 - 27, 2018

Novi Sad, Serbia

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Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Moghaddam SAbbaspour M(2023)Friendship Preference: Scalable and Robust Category of Features for Social Bot DetectionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315900720:2(1516-1528)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TDSC.2022.3159007
Ellaky ZBenabbou FOuahabi S(2023)Systematic Literature Review of Social Media Bots Detection SystemsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.04.00435:5(101551)Online publication date: May-2023
https://doi.org/10.1016/j.jksuci.2023.04.004
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
Chaudhari DPawar A(2022)A Systematic Comparison of Machine Learning and NLP Techniques to Unveil Propaganda in Social MediaJournal of Information Technology Research10.4018/JITR.29938415:1(1-14)Online publication date: 1-Jan-2022
https://doi.org/10.4018/JITR.299384
Sangeethapriya RAkilandeswari J(2022)Identify Twitter Data from Humans or Bots Using Machine Learning Algorithms with Kendalls CorrelationEvolution in Computational Intelligence10.1007/978-981-16-6616-2_19(203-212)Online publication date: 24-Apr-2022
https://doi.org/10.1007/978-981-16-6616-2_19
Pinheiro LPereira MAndrade ENunes LAbreu WPinheiro PHolanda Filho RPinheiro P(2021)An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency VirusApplied Sciences10.3390/app11211045711:21(10457)Online publication date: 7-Nov-2021
https://doi.org/10.3390/app112110457
Qureshi KMalick RSabih M(2021)Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and RecommendationIEEE Access10.1109/ACCESS.2021.31144179(137744-137781)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3114417
Chaudhari DPawar A(2021)Propaganda analysis in social media: a bibliometric reviewInformation Discovery and Delivery10.1108/IDD-06-2020-006549:1(57-70)Online publication date: 29-Jan-2021
https://doi.org/10.1108/IDD-06-2020-0065
Fürst S(2021)Neue Öffentlichkeitsdynamiken: Zu selbstverstärkenden, plattformübergreifenden Effekten von ‚Popularität‘Digitaler Strukturwandel der Öffentlichkeit10.1007/978-3-658-32133-8_19(339-359)Online publication date: 2-Apr-2021
https://doi.org/10.1007/978-3-658-32133-8_19
Meneses Silva CSilva Fontes RColaço Júnior M(2020)Intelligent Fake News Detection: A Systematic MappingJournal of Applied Security Research10.1080/19361610.2020.1761224(1-22)Online publication date: 14-May-2020
https://doi.org/10.1080/19361610.2020.1761224
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