research-article

BotCamp: Bot-driven Interactions in Social Campaigns

Authors:

Noor Abu-El-Rub,

Abdullah MueenAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 2529 - 2535

https://doi.org/10.1145/3308558.3313420

Published: 13 May 2019 Publication History

Abstract

Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following).

In this work, we detect a large number of bots interested in politics. We perform multi-aspect (i.e. temporal, textual, and topographical) clustering of bots, and ensemble the clusters to identify campaigns of bots. We observe similarity among the bots in a campaign in various aspects such as temporal correlation, sentimental alignment, and topical grouping. However, we also discover bots compete in gaining attention from humans and occasionally engage in arguments. We classify such bot interactions in two primary groups: agreeing (i.e. positive) and disagreeing (i.e. negative) interactions and develop an automatic interaction classifier to discover novel interactions among bots participating in social campaigns.

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Cited By

Akhtar MMasood RIkram MKanhere SQuek TGao DZhou JCardenas A(2024)SoK: False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media ManipulationsProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3644998(1784-1800)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3644998
Zeng FSun YLi Y(2023)MRLBot: Multi-Dimensional Representation Learning for Social Media Bot DetectionElectronics10.3390/electronics1210229812:10(2298)Online publication date: 19-May-2023
https://doi.org/10.3390/electronics12102298
Kausar STahir BMehmood M(2023)Manipify: An Automated Framework for Detecting Manipulators in Twitter TrendsJournal of Social Computing10.23919/JSC.2023.00014:1(46-61)Online publication date: Mar-2023
https://doi.org/10.23919/JSC.2023.0001
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Published In

cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

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 ACM 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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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Akhtar MMasood RIkram MKanhere SQuek TGao DZhou JCardenas A(2024)SoK: False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media ManipulationsProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3644998(1784-1800)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3644998
Zeng FSun YLi Y(2023)MRLBot: Multi-Dimensional Representation Learning for Social Media Bot DetectionElectronics10.3390/electronics1210229812:10(2298)Online publication date: 19-May-2023
https://doi.org/10.3390/electronics12102298
Kausar STahir BMehmood M(2023)Manipify: An Automated Framework for Detecting Manipulators in Twitter TrendsJournal of Social Computing10.23919/JSC.2023.00014:1(46-61)Online publication date: Mar-2023
https://doi.org/10.23919/JSC.2023.0001
Sun LRao YWu LZhang XLan YNazir A(2023)Fighting False Information from Propagation Process: A SurveyACM Computing Surveys10.1145/356338855:10(1-38)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3563388
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.2023.04.00435:5Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2023.04.004
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
https://doi.org/10.1007/s11042-023-14730-5
Emmer M(2023)Soziale Medien in der politischen KommunikationHandbuch Soziale Medien10.1007/978-3-658-25995-2_5(57-80)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-658-25995-2_5
Yang ZChen XWang HWang WMiao ZJiang T(2022)A New Joint Approach with Temporal and Profile Information for Social Bot DetectionSecurity and Communication Networks10.1155/2022/91193882022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9119388
Chakraborty MDas SMamidi R(2022)Detection of Fake Users in Twitter Using Network Representation and NLP2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS53615.2022.9668371(754-758)Online publication date: 4-Jan-2022
https://doi.org/10.1109/COMSNETS53615.2022.9668371
Kirn SHinders M(2022)Ridge count thresholding to uncover coordinated networks during onset of the Covid-19 pandemicSocial Network Analysis and Mining10.1007/s13278-022-00873-012:1Online publication date: 25-Mar-2022
https://doi.org/10.1007/s13278-022-00873-0
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