Uncovering Fake Likers in Online Social Networks
Abstract
As the commercial implications of Likes in online social networks multiply, the number of fake Likes also increase rapidly. To maintain a healthy ecosystem, however, it is critically important to prevent and detect such fake Likes. Toward this goal, in this paper, we investigate the problem of detecting the so-called "fake likers" who frequently make fake Likes for illegitimate reasons. To uncover fake Likes in online social networks, we: (1) first collect a substantial number of profiles of both fake and legitimate Likers using linkage and honeypot approaches, (2) analyze the characteristics of both types of Likers, (3) identify effective features exploiting the learned characteristics and apply them in supervised learning models, and (4) thoroughly evaluate their performances against three baseline methods and under two attack models. Our experimental results show that our proposed methods with effective features significantly outperformed baseline methods, with accuracy = 0.871, false positive rate = 0.1, and false negative rate = 0.14.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The location result supports Facebook's claim that the percentage of accounts that are duplicate or false is meaningfully lower in developed markets such as the United States or United Kingdom and higher in developing markets such as India and Turkey.
“The location result supports Facebook's claim that 'the percentage of accounts that are duplicate or false is meaningfully lower in developed markets such as the United States or United Kingdom and higher in developing markets such as India and Turkey.' 3”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [3]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884 · see citing works
A Comparative Study on Feature Selection in Text Categorization
Yang, Y., & Pedersen, J. (1997) · Cited by 4,749 · see citing works
xgboost: Extreme Gradient Boosting
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al. (2014) · doi:10.32614/cran.package.xgboost · Cited by 2,735 · see citing works
Detecting spammers on social networks
Stringhini, G., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 26th Annual Computer Security Applications Conference · doi:10.1145/1920261.1920263 · Cited by 791 · see citing works
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter
Lee, K., Eoff, B. D., & Caverlee, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14106 · Cited by 448 · see citing works
Self-citationCopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Beutel, A., Xu, W., Guruswami, V., Palow, C., & Faloutsos, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488400 · Cited by 335 · see citing works
Uncovering Large Groups of Active Malicious Accounts in Online Social Networks
Cao, Q., Yang, X., Yu, J., & Palow, C. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660269 · Cited by 274 · see citing works
Trafficking fraudulent accounts: the role of the underground market in Twitter spam and abuse
Thomas, K., McCoy, D., Grier, C., Kolcz, A., & Paxson, V. (2013) · Cited by 239 · see citing works
Towards Detecting Anomalous User Behavior in Online Social Networks
Viswanath, B., Gummadi, K. P., Bashir, M. A., Krishnamurthy, B., Crovella, M. E., Guha, S., & Mislove, A. E. (2014) · Cited by 236 · see citing works
Serf and turf: crowdturfing for fun and profit
Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., & Zhao, B. Y. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187928 · Cited by 197 · see citing works
Jang, J. Y., Han, K., Shih, P. C., & Lee, D. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702555 · Cited by 121 · see citing works
Self-citationDirty jobs: the role of freelance labor in web service abuse
Motoyama, M., McCoy, D., Levchenko, K., Savage, S., & Voelker, G. M. (2011) · Cited by 94 · see citing works
De Cristofaro, E., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663729 · Cited by 85 · cited 2× · see citing works
“Note that most of fake Likes were delivered within three days in a bursty fashion, that is consistent with the finding in [5].”
BackgroundResultLee, K., Tamilarasan, P., & Caverlee, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14384 · Cited by 81 · see citing works
Self-citationStringhini, G., Egele, M., Kruegel, C., & Vigna, G. (2012) · doi:10.1145/2342549.2342551 · Cited by 74 · see citing works
Lee, K., Webb, S., & Ge, H. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14528 · Cited by 39 · see citing works
Self-citationPay me and i'll follow you: detection of crowdturfing following activities in microblog environment
Liu, Y., Liu, Y., Zhang, M., & Ma, S. (2016) · International Joint Conference on Artificial Intelligence · Cited by 16 · see citing works
Lee, K., Webb, S., & Ge, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.0574 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
A review on social spam detection: Challenges, open issues, and future directions
Rao, S., Verma, A. K., & Bhatia, T. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115742 · Cited by 225 · see citing works
A Survey of Defensive Deception: Approaches Using Game Theory and Machine Learning
Zhu, M., Anwar, A. H., Wan, Z., Cho, J.-H., Kamhoua, C., & Singh, M. P. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3102874 · Cited by 133 · see citing works
Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter
Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019) · ACM Transactions on the Web · doi:10.1145/3313184 · Cited by 105 · see citing works
A prediction system of Sybil attack in social network using deep-regression model
Al‐Qurishi, M., Alrubaian, M., Rahman, S. M. M., Alamri, A. M., & Hassan, M. M. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.030 · Cited by 71 · see citing works
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2019) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3292522.3326030 · Cited by 52 · see citing works
Fletcher, K. K. (2019) · IEEE International Conference on Services Computing · doi:10.1109/scc.2019.00014 · Cited by 51 · see citing works
Sen, I., Aggarwal, A., Mian, S., Singh, S., Kumaraguru, P., & Datta, A. (2018) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3201064.3201105 · Cited by 50 · see citing works
Online Social Deception and Its Countermeasures: A Survey
Guo, Z. Y., Cho, J.-H., Chen, I.-R., Sengupta, S., Hong, M., & Mitra, T. (2020) · IEEE Access · doi:10.1109/access.2020.3047337 · Cited by 45 · see citing works
A Multidimensional Trust Evaluation Framework for Online Social Networks Based on Machine Learning
Chen, X., Yuan, Y., Lu, L., & Yang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2957779 · Cited by 42 · see citing works
A perspective on complexity and networks science
Caldarelli, G. (2020) · Journal of Physics Complexity · doi:10.1088/2632-072x/ab9a24 · Cited by 33 · see citing works
Measuring, Characterizing, and Detecting Facebook Like Farms
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2017) · ACM Transactions on Privacy and Security · doi:10.1145/3121134 · Cited by 27 · see citing works
“Finally, we highlight that our work can complement other methods used in prior work to detect fake and compromised accounts, such as using unsupervised anomaly detection techniques [50], temporal features [23, 24], IP addresses [45], as well as generic supervised learning [3].”
MethodDetecting Crowdturfing "Add to Favorites" Activities in Online Shopping
Su, N., Liu, Y., Li, Z., Liu, Y., Zhang, M., & Ma, S. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186079 · Cited by 26 · see citing works
The Art and Craft of Fraudulent App Promotion in Google Play
Rahman, M., Hernandez, N., Recabarren, R., Ahmed, S. I., & Carbunar, B. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3319535.3345658 · Cited by 24 · see citing works
Detecting and Analyzing Collusive Entities on YouTube
Dutta, H. S., Jobanputra, M., Negi, H., & Chakraborty, T. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3477300 · Cited by 24 · see citing works
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on TwitterPreprint
Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04406 · Cited by 18 · see citing works
Learning-Based Model to Fight against Fake Like Clicks on Instagram Posts
Thejas, G. S., Soni, J., Chandna, K., Iyengar, S. S., Sunitha, N. R., & Prabakar, N. (2019) · IEEE SoutheastCon · doi:10.1109/southeastcon42311.2019.9020533 · Cited by 17 · see citing works
Fraud De-Anonymization for Fun and Profit
Hernandez, N., Rahman, M., Recabarren, R., & Carbunar, B. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243770 · Cited by 16 · see citing works
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot DetectionPreprint
Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05132 · Cited by 17 · see citing works
Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A SurveyPreprint
Zhu, M., Anwar, A. H., Wan, Z., Cho, J.-H., Kamhoua, C., & Singh, M. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10121 · Cited by 14 · see citing works
Blackmarket-Driven Collusion on Online Media: A Survey
Dutta, H. S., & Chakraborty, T. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3517931 · Cited by 13 · see citing works
Kotenko, I. V., Vitkova, L., Saenko, I. B., Tushkanova, O. N., & Branitskiy, A. A. (2020) · AI Communications · doi:10.3233/aic-200647 · Cited by 11 · see citing works
SAFER: Social Capital-Based Friend Recommendation to Defend against Phishing Attacks
Guo, Z. Y., Cho, J.-H., Chen, I.-R., Sengupta, S., Hong, M., & Mitra, T. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19288 · Cited by 11 · see citing works
Evolving Cyber Threats, Combating Techniques, and Open Issues in Online Social Networks
Rao, S., Verma, A. K., & Bhatia, T. (2020) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-5728-0.ch012 · Cited by 9 · see citing works
Understanding Promotion-as-a-Service on GitHub
Du, K., Yang, H., Zhang, Y., Duan, H., Wang, H., Hao, S., Li, Z., & Yang, M. (2020) · Annual Computer Security Applications Conference · doi:10.1145/3427228.3427258 · Cited by 9 · see citing works
Search Rank Fraud De-Anonymization in Online Systems
Rahman, M., Hernandez, N., Carbunar, B., & Chau, D. H. (2018) · Proceedings of the 29th on Hypertext and Social Media · doi:10.1145/3209542.3209555 · Cited by 8 · see citing works
Approach to Identification and Analysis of Information Sources in Social Networks
Vitkova, L., & Kolomeets, M. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-32258-8_34 · Cited by 7 · see citing works
Towards Linking Camouflaged Descriptions to Implicit Products in E-commerce
Huang, L., Bo, Y., Zhang, R., & Lu, Q. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401067 · Cited by 6 · see citing works
Finding Camouflaged Needle in a Haystack?
He, G., Kang, Y., Gao, Z., Jiang, Z., Sun, C., Liu, X., Lu, W. P., Zhang, Q., & Si, L. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331197 · Cited by 6 · see citing works
Recommending Inferior Results: A General and Feature-Free Model for Spam Detection
Liu, Y. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411900 · Cited by 6 · see citing works
Vitkova, L., Saenko, I. B., & Tushkanova, O. N. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-32258-8_29 · Cited by 5 · see citing works
Towards De-Anonymization of Google Play Search Rank Fraud
Rahman, M. M., Hernandez, N., Cărbunar, B., & Chau, D. H. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2975170 · Cited by 6 · see citing works
Between Overload and Indifference: Detection of Fake Accounts and Social Bots by Community Managers
Boberg, S., Frischlich, L., Schatto‐Eckrodt, T., Wintterlin, F., & Quandt, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39627-5_2 · Cited by 4 · see citing works
Hernandez, 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
Vitkova, L., Kotenko, I. V., Kolomeets, M., Tushkanova, O. N., & Chechulin, A. A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-50097-9_42 · Cited by 3 · see citing works
Emotions and Interests of Evolving Twitter Bots
Fazzolari, M., Pratelli, M., Martinelli, F., & Petrocchi, M. (2020) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais48028.2020.9122703 · Cited by 3 · see citing works
Identifying 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
Measuring, Characterizing, and Detecting Facebook Like FarmsPreprint
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, D., & Shafiq, M. Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00190 · Cited by 3 · see citing works
Stateless Puzzles for Real Time Online Fraud Preemption
Rahman, M., Recabarren, R., Carbunar, B., & Lee, D. (2017) · Proceedings of the 2017 ACM on Web Science Conference · doi:10.1145/3091478.3091507 · Cited by 4 · see citing works
Self-citationI Like Your Tagged Photos, But Do We Know Each Other?: Analyzing the Role of Tags in Like Networks
Park, H., Song, J., Han, K., & Kim, S. (2018) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws.2018.00055 · Cited by 2 · see citing works
Braz, P. A., & Goldschmidt, R. R. (2017) · Simpósio Brasileiro de Cibersegurança (SBSeg) · doi:10.5753/sbseg.2017.19524 · Cited by 2 · see citing works
Bachelor of Engineering, C. S. F. L. I., Kadam, N., Patidar, H., & Bachelor of Engineering, C. S. A. E. D. (2020) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.f8417.038620 · Cited by 3 · see citing works
Braz, P. A., & Goldschmidt, R. R. (2018) · Simpósio Brasileiro de Cibersegurança (SBSeg) · doi:10.5753/sbseg.2018.4262
Search Rank Fraud Prevention in Online Systems
Rahman, M. M. (2018) · doi:10.25148/etd.fidc006998
- Authors
- Badri Satya, P. R., Lee, K., Lee, D., Tran, T., & Zhang, J. (.
- Affiliations
- Utah State University, Logan, UT, USA
- Utah State University (US)
- Pennsylvania State University (US)
- The Pennsylvania State University, University Park, PA, USA
- Venue
- Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2016-10-24
- Pages
- 2365-2370
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2535518076
- Cited by
- 46
- References
- 18
- Citations by year
- 2022: 1 · 2021: 7 · 2020: 14 · 2019: 10 · 2018: 8 · 2017: 6
- FWCI
- 6.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Funders
- NSF (CNS-1422215, IUSE-1525601, CNS-1553035)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Keywords
- social networking sites, fake account detection, honeypots, user profiling, malicious user detection
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 ›
Are you an author, or do you see a problem with this page? Request a review
