research-article

Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots

Authors:

Emiliano De Cristofaro,

Guillaume Jourjon,

Mohamed Ali Kaafar,

M. Zubair ShafiqAuthors Info & Claims

IMC '14: Proceedings of the 2014 Conference on Internet Measurement Conference

Pages 129 - 136

https://doi.org/10.1145/2663716.2663729

Published: 05 November 2014 Publication History

Abstract

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods.

This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior.

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Cited By

He PZhang XLin CWang TJi S(2024)Towards understanding bogus traffic service in online social networks在线社交网络中的虚假流量服务挖掘Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230006825:3(415-431)Online publication date: 23-Mar-2024
https://doi.org/10.1631/FITEE.2300068
Ling CStringhini G(2024)"It was jerks on the Internet being jerks on the Internet": Understanding Zoombombing Through the Eyes of Its VictimsProceedings of the 2024 European Symposium on Usable Security10.1145/3688459.3688466(261-276)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3688459.3688466
Soro FFavale TGiordano DDrago IRescio TMellia MHouidi ZRossi D(2023)Enlightening the Darknets: Augmenting Darknet Visibility With Active ProbesIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326767120:4(5012-5025)Online publication date: Dec-2023
https://doi.org/10.1109/TNSM.2023.3267671
Show More Cited By

Index Terms

Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

cover image ACM Conferences

IMC '14: Proceedings of the 2014 Conference on Internet Measurement Conference

November 2014

524 pages

ISBN:9781450332132

DOI:10.1145/2663716

General Chair:
Carey Williamson
University of Calgary, Canada
,
Program Chairs:
Aditya Akella
University of Wisconsin - Madison, USA
,
Nina Taft
Google, USA

Copyright © 2014 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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SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGCOMM: ACM Special Interest Group on Data Communication
USENIX Assoc: USENIX Assoc

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2014

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IMC '14

Sponsor:

SIGMETRICS
SIGCOMM
USENIX Assoc

IMC '14: Internet Measurement Conference

November 5 - 7, 2014

BC, Vancouver, Canada

Acceptance Rates

IMC '14 Paper Acceptance Rate 32 of 103 submissions, 31%;

Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

He PZhang XLin CWang TJi S(2024)Towards understanding bogus traffic service in online social networks在线社交网络中的虚假流量服务挖掘Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230006825:3(415-431)Online publication date: 23-Mar-2024
https://doi.org/10.1631/FITEE.2300068
Ling CStringhini G(2024)"It was jerks on the Internet being jerks on the Internet": Understanding Zoombombing Through the Eyes of Its VictimsProceedings of the 2024 European Symposium on Usable Security10.1145/3688459.3688466(261-276)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3688459.3688466
Soro FFavale TGiordano DDrago IRescio TMellia MHouidi ZRossi D(2023)Enlightening the Darknets: Augmenting Darknet Visibility With Active ProbesIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326767120:4(5012-5025)Online publication date: Dec-2023
https://doi.org/10.1109/TNSM.2023.3267671
Fujii SSato TAoki STsuda YKawaguchi NShigemoto TTerada M(2023)Stargazer: Long-term and Multiregional Measurement of Timing/Geolocation-based CloakingIEEE Access10.1109/ACCESS.2023.3280815(1-1)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3280815
Nevado-Catalán DPastrana SVallina-Rodriguez NTapiador J(2023)An analysis of fake social media engagement servicesComputers & Security10.1016/j.cose.2022.103013124(103013)Online publication date: Jan-2023
https://doi.org/10.1016/j.cose.2022.103013
Bardi SConti MPajola LTricomi P(2023)Social Honeypot for Humans: Luring People Through Self-managed Instagram PagesApplied Cryptography and Network Security10.1007/978-3-031-33488-7_12(309-336)Online publication date: 29-May-2023
https://doi.org/10.1007/978-3-031-33488-7_12
Dutta HChakraborty T(2022)Blackmarket-Driven Collusion on Online Media: A SurveyACM/IMS Transactions on Data Science10.1145/35179312:4(1-37)Online publication date: 17-May-2022
https://dl.acm.org/doi/10.1145/3517931
Edu JMulligan CPierazzi FPolakis JSuarez-Tangil GSuch JBarakat CPelsser CBenson TChoffnes D(2022)Exploring the security and privacy risks of chatbots in messaging servicesProceedings of the 22nd ACM Internet Measurement Conference10.1145/3517745.3561433(581-588)Online publication date: 25-Oct-2022
https://dl.acm.org/doi/10.1145/3517745.3561433
Jung SSalminen JJ. Jansen BBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)The Effect of Hiding Dislikes on the Use of YouTube's Like and Dislike FeaturesProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531546(202-207)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531546
Favale TGiordano DDrago IMellia M(2022)What Scanners do at L7? Exploring Horizontal Honeypots for Security Monitoring2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW55150.2022.00037(307-313)Online publication date: Jun-2022
https://doi.org/10.1109/EuroSPW55150.2022.00037
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