Paying for Likes?
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.
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Specialized fraud workers control multiple user accounts and seek employment from product developers to post fake reviews or activities.
“Specialized fraud workers (also referred to as fraud freelancers, or fraudsters) control multiple user accounts and seek employment by product developers to post fake reviews or activities for their products.”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [13]
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Self-citation
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MethodLearning-Based Model to Fight against Fake Like Clicks on Instagram Posts
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“Previous works on honeypots have covered many aspects, such as (i) introducing new hon-eypots that target specific protocols or services [26], [27], (ii) evaluating the effectiveness of different types of honey-pots [28], and (iii) presenting techniques to detect honeypots [29], [30].”
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Measurement and Early Detection of Third-Party Application Abuse on Twitter
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Erdin, E., Klukovich, E., & Güneş, M. H. (2015) · doi:10.1109/lcnw.2015.7365914 · Cited by 4 · see citing works
“look into how liking fraud on Facebook is typically done bot farms to promote Facebook pages with honeypots [20].”
BackgroundDeKoven, L. F., Pottinger, T., Savage, S., Voelker, G. M., & Leontiadis, N. (2018) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3278532.3278537 · Cited by 4 · see citing works
“For example, De Cristofaro et al’s analysis of Facebook services [5]”
BackgroundMeasuring and mitigating OAuth access token abuse by collusion networks
Farooqi, S., Zaffar, F., Leontiadis, N., & Shafiq, Z. (2020) · Communications of the ACM · doi:10.1145/3387720 · Cited by 4 · see citing works
EDGE: An Enticing Deceptive-content GEnerator as Defensive Deception
Li, H., Guo, Y., Huo, S., & Yuehang, D. I. N. G. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.05.017 · Cited by 4 · see citing works
Meixner, B., & Marlow, J. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053119 · Cited by 4 · see citing works
“At the same time, work looking at the darker side of “likes” have noted that the way a social media site uses “likes” to promote or favor content in its algorithmic feed can lead to cases where page owners try to game the system, for example using “like-farming” tactics [3].”
BackgroundHernandez, N., Recabarren, R., Carbunar, B., & Ahmed, S. I. (2021) · Proceedings of the 21st ACM Internet Measurement Conference · doi:10.1145/3487552.3487837 · Cited by 3 · see citing works
Scalable Similarity Joins of Tokenized Strings
Metwally, A., & Huang, C.-H. (2019) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2019.00193 · Cited by 4 · see citing works
“Filtering abusive ads traffic was studied in [15], [16], [41], [44], [46], [58], and fake likes and promotion on social networks was studied in [17], [28].”
BackgroundIdentifying and Quantifying Coordinated Manipulation of Upvotes and Downvotes in Naver News Comments
Jeong, J., Kang, J., & Moon, S. B. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7301 · Cited by 3 · see citing works
“For example, fake likes on Facebook (De Cristofaro et al. 2014; Badri Satya et al. 2016), fake views on YouTube (Chen, Zhou, and Chiu 2015; Keller 2018), or fake streams on Spotify (Leight 2019) are a few examples.”
BackgroundCombating Fraud in Online Social Networks: Detecting Stealthy Facebook Like FarmsPreprint
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00506 · Cited by 3 · see citing works
“Specifically, [11] analyzes the geographic and demographic distribution of garnered likes, the temporal patterns observed for each campaign, as well as the social graph induced by the likers.”
BackgroundMethodInfluentialSelf-citationThe Art of Social Bots: A Review and a Refined TaxonomyPreprint
Latah, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03240 · Cited by 3 · see citing works
“They may also attract fake profiles by means of paid campaigns De Cristofaro et al. (2014).”
BackgroundHe, H., Yang, H., Burckhardt, P., Kapravelos, A., Vasilescu, B., & Kästner, C. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3764531 · Cited by 2 · see citing works
Ling, C., & Stringhini, G. (2024) · European Symposium on Usable Security (EuroUSEC) · doi:10.1145/3688459.3688466 · Cited by 2 · see citing works
Social Honeypot for Humans: Luring People Through Self-managed Instagram Pages
Bardi, S., Conti, M., Pajola, L., & Tricomi, P. P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33488-7_12 · Cited by 3 · see citing works
Where art thou, Eve? Experiences laying traps for Internet eavesdroppers
Vaidya, T., Burger, E., Sherr, M., et al. (2017) · USENIX Security Symposium · Cited by 2 · see citing works
What Scanners do at L7? Exploring Horizontal Honeypots for Security Monitoring
Favale, T., Giordano, D., Drago, I., et al. (2022) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw55150.2022.00037 · Cited by 2 · see citing works
Analysis of Malicious Behavior on Social Media Platforms Using Agent-Based Modeling
Onuchowska, A. (2020) · Digital Commons - University of South Florida (University of South Florida) · Cited by 2 · see citing works
Towards understanding bogus traffic service in online social networks
He, P., Zhang, X., Lin, C., Wang, T., & Ji, S. (2024) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300068 · Cited by 1 · see citing works
“Previous research has studied Twitter spam markets (Thomas et al., 2013), Twitter follower markets (Stringhini et al., 2013), Facebook like farms (de Cristofaro et al., 2014), and collusive retweeting markets (Dutta and Chakraborty, 2020).”
eWOM Marketing Techniques for Competitive Advantage
Bhattacharya, A. (2015) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-4666-9449-1.ch002 · Cited by 1 · see citing works
Meixner, B., & Marlow, J. (2017) · Cited by 1 · see citing works
Stargazer: Long-term and Multiregional Measurement of Timing/Geolocation-based Cloaking
Fujii, S., Sato, T., Aoki, S., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3280815 · Cited by 1 · see citing works
“…other roles such as malware distribution [3], [4], phishing [5], [6], [7], fraudulent The associate editor coordinating the review of this manuscript and approving it for publication was Lei Shu . services [8], [9] spam distribution [10], and fake Anti-Virus (AV) scanner distribution [11], [12].”
BackgroundEstimating Creditworthiness using Uncertain Online Data
Bolhuis, M. (2015) · Cited by 1 · see citing works
Analysis of user popularity pattern and engagement prediction in online social networks
Mohammadi, S. (2018) · doi:10.70675/dc616dd7zc772z45ecz82d2z8b3f1315e7a0 · Cited by 1 · see citing works
Tracking Triadic Cardinality Distributions for Burst Detection in Social Activity StreamsPreprint
Zhao, J., Lui, J. C. S., Towsley, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.3808
“Spamming social bots have been observed their prevalent existences in both Twitter and Facebook recently [10]–[15].”
BackgroundCombating Fraud in Online Social Networks: Characterizing and Detecting Facebook Like FarmsPreprint
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. (2015) · arXiv (Cornell University)
Self-citationFake Likers Detection on Facebook
Satya, B., & Satya, P. R. B. (2016)
Scalable Similarity Joins of Tokenized StringsPreprint
Metwally, A., & Huang, C.-H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.09238
“Filtering abusive ads traffic was studied in [15], [16], [41], [44], [46], [58], and fake likes and promotion on social networks was studied in [17], [28].”
BackgroundPoster: Using Honeypots to Understand Attacks to Industrial Control Systems
Pogliani, M., Barlocco, M., Pesatori, M., et al. (2019) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha)
Mensa, M., Deng, T., & Thompson, L. (2025) · Sexuality & Culture · doi:10.1007/s12119-025-10490-6
“Instagram is recognized for the importance of likes and followers as indicators of an account’s popularity and potential profitability (De Cristofaro et al., 2014; De Vries, 2019 ).”
Maras, M., Logie, K., & Miranda, M. D. (2026) · Journal of Economic Criminology · doi:10.1016/j.jeconc.2026.100250
Accountability Gaps in the Fake Follower Market
Kalameyets, M., Owens, R., Hammouchi, H., Sergeeva, A., Hoehn, S., Jalilzade, E., Wang, J., Patel, S., Aidinlis, S., Farrand, B., et al. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806758
- Authors
- De Cristofaro, E., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z.
- Affiliations
- University College London, London, United Kingdom
- Data61 (AU)
- NICTA, Sydney, Australia
- The University of Iowa, Iowa City, IA, USA
- Venue
- Proceedings of the 2014 Conference on Internet Measurement Conference
- Publisher
- ACM
- Published
- 2014-11-05
- Pages
- 129-136
- OpenAlex
- W1975219037
- Cited by
- 85
- References
- 17
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 8 · 2020: 10 · 2019: 14 · 2018: 11 · 2017: 13 · 2016: 5 · 2015: 8 · 2014: 1
- FWCI
- 7.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- Facebook likes, social media marketing, Facebook advertising, popularity metrics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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