Detection of Fake Profiles on Online Social Network Platforms: Performance Evaluation of Artificial Intelligence Techniques
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SMOTE has been extensively used to handle class imbalance in fake account detection tasks to improve model generalizability.
“Likewise, SMOTE has been extensively used to handle the class imbalance issue in fake account detection tasks to improve model generalizability56.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [56]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243 · see citing works
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140 · see citing works
Generalization of back-propagation to recurrent neural networks
Pineda, F. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.2229 · Cited by 931 · see citing works
Fake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772 · cited 3× · see citing works
“Online social network (OSN) platforms are the most prevalent and rapidly disseminating information applications on the Internet today [1].”
Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 461 · cited 2× · see citing works
“There were two datasets used: the Twitter dataset [42, 43] and the Instagram dataset [32].”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381 · cited 2× · see citing works
“There were two datasets used: the Twitter dataset [42, 43] and the Instagram dataset [32].”
Online Social Networks: Threats and Solutions
Fire, M., Goldschmidt, R., & Elovici, Y. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2321628 · Cited by 359 · see citing works
Logistic regression in data analysis: an overview
Maalouf, M. (2011) · International Journal of Data Analysis Techniques and Strategies · doi:10.1504/ijdats.2011.041335 · Cited by 353 · see citing works
Using Machine Learning to Detect Fake Identities: Bots vs Humans
van der Walt, E., & Eloff, J. H. P. (2018) · IEEE Access · doi:10.1109/access.2018.2796018 · Cited by 187 · see citing works
Fake profile detection techniques in large-scale online social networks: A comprehensive review
Devakunchari, R., & Valliyammai, C. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.05.020 · Cited by 157 · see citing works
Random Walk Based Fake Account Detection in Online Social Networks
Jia, J., Wang, B., & Gong, N. Z. (2017) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2017.55 · Cited by 147 · see citing works
Cyberbullying Detection on Social Networks Using Machine Learning Approaches
Islam, M. M., Uddin, M. A., Islam, L., Akter, A., Sharmin, S., & Acharjee, U. K. (2020) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde50874.2020.9411601 · Cited by 133 · see citing works
Instagram Fake and Automated Account Detection
Akyön, F. Ç., & Kalfaoglu, M. E. (2019) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu48272.2019.8946437 · Cited by 118 · see citing works
Twitter fake account detection
Erşahin, B., Aktaş, Ö., Kılınç, D., & Akyol, C. K. (2017) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2017.8093420 · Cited by 114 · see citing works
Sifting robotic from organic text: A natural language approach for detecting automation on Twitter
Clark, E. M., Williams, J. R., Jones, C. A., Galbraith, R. A., Danforth, C. M., & Dodds, P. S. (2015) · Journal of Computational Science · doi:10.1016/j.jocs.2015.11.002 · Cited by 109 · see citing works
Fake Profile Detection on Social Networking Websites: A Comprehensive Review
Roy, P. K., & Chahar, S. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3064901 · Cited by 100 · see citing works
DeepProfile: Finding fake profile in online social network using dynamic CNN
Wanda, P., & Jie, H. J. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102465 · Cited by 84 · see citing works
Classification of various attacks and their defence mechanism in online social networks: a survey
Sahoo, S. R., & Gupta, B. B. (2019) · Enterprise Information Systems · doi:10.1080/17517575.2019.1605542 · Cited by 80 · see citing works
Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders
Shahid, W., Li, Y., Staples, D., Gilkar, G. A., Hakak, S., & Ghorbani, A. A. (2022) · IEEE Access · doi:10.1109/access.2022.3157724 · Cited by 77 · see citing works
Detecting Spam Accounts on Twitter
Alom, Z., Carminati, B., & Ferrari, E. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508495 · Cited by 70 · see citing works
Detection of Fake Profile in Online Social Networks Using Machine Learning
Singh, N. D., Sharma, T., Thakral, A., & Choudhury, T. (2018) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce.2018.8441713 · Cited by 60 · see citing works
TSD: Detecting Sybil Accounts in Twitter
Alsaleh, M., Alarifi, A., Al‐Salman, A. S., Alfayez, M., & Almuhaysin, A. (2014) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2014.81 · Cited by 58 · see citing works
Fake profile recognition using big data analytics in social media platforms
Awan, M. J., Khan, M. A., Ansari, Z. K., Yasin, A., & Shehzad, H. M. F. (2022) · International Journal of Computer Applications in Technology · doi:10.1504/ijcat.2022.124942 · Cited by 52 · see citing works
An Efficient Method for Detection of Fake Accounts on the Instagram Platform
Sheikhi, S. (2020) · Revue d intelligence artificielle · doi:10.18280/ria.340407 · Cited by 50 · see citing works
Spam detection on social networks using deep contextualized word representation
Bakır, R., & Erbay, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13397-8 · Cited by 48 · see citing works
Fake Accounts Detection on Twitter Using Blacklist
Swe, M. M., & Myo, N. N. (2018) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2018.8466499 · Cited by 47 · see citing works
Contents-Based Spam Detection on Social Networks Using RoBERTa Embedding and Stacked BLSTM
Ghanem, R., Erbay, H., & Bakour, K. (2023) · SN Computer Science · doi:10.1007/s42979-023-01798-x · Cited by 40 · see citing works
Mughaid, A., Obeidat, I., AlZu’bi, S., Elsoud, E. A., Alnajjar, A., Alsoud, A. R., & Abualigah, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14347-8 · Cited by 39 · see citing works
A novel machine learning‐based framework for detecting fake Instagram profiles
Kaushik, K., Bhardwaj, A., Kumar, M., Gupta, S. K., & Gupta, A. (2022) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.7349 · Cited by 37 · see citing works
Fake account detection in twitter using logistic regression with particle swarm optimization
Bharti, K. K., & Pandey, S. (2021) · Soft Computing · doi:10.1007/s00500-021-05930-y · Cited by 36 · see citing works
UbCadet: detection of compromised accounts in twitter based on user behavioural profiling
Savyan, P. V., & Bhanu, S. M. S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08721-z · Cited by 30 · see citing works
Detecting Malicious Users in Twitter using Classifiers
Singh, M., Bansal, D., & Sofat, S. (2014) · International Conference on Security of Information and Networks (SIN) · doi:10.1145/2659651.2659736 · Cited by 28 · see citing works
Features combination for the detection of malicious Twitter accounts
David, I., Siordia, O. S., & Moctezuma, D. (2016) · IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) · doi:10.1109/ropec.2016.7830626 · Cited by 28 · see citing works
N-Gram Based Sarcasm Detection for News and Social Media Text Using Hybrid Deep Learning Models
Thaokar, C. B., Rout, J. K., Rout, M., & Ray, N. K. (2024) · SN Computer Science · doi:10.1007/s42979-023-02506-5 · Cited by 28 · see citing works
Detection of Malicious Profiles and Protecting Users in Online Social Networks
Senthil Raja, M., & Arun Raj, L. (2022) · Wireless Personal Communications · doi:10.1007/s11277-021-08095-x · Cited by 25 · see citing works
DeepMUI : A novel method to identify malicious users on online social network platforms
Shah, A., Varshney, S., & Mehrotra, M. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7917 · Cited by 11 · see citing works
Self-citationRevathi, S., & Suriakala, M. (2018) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss.2018.8768751 · Cited by 9 · see citing works
Identification of Fake News Using Deep Neural Network-Based Hybrid Model
Gupta, S., Verma, B., Gupta, P., Goel, L., Yadav, A., & Yadav, D. (2023) · SN Computer Science · doi:10.1007/s42979-023-02117-0 · Cited by 9 · see citing works
Ambareen, K., & Sundaram, S. M. (2023) · SN Computer Science · doi:10.1007/s42979-023-02301-2 · Cited by 6 · see citing works
Priyadharshini, V., & Valarmathi, A. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-202937 · Cited by 4 · see citing works
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Papageorgiou, E., Chronis, C., Varlamis, I., & Himeur, Y. (2024) · Future Internet · doi:10.3390/fi16080298 · Cited by 73 · see citing works
Instagram fake profile detection using an ensemble learning method
Goyal, B., Gill, N. S., Gulia, P., Alduaiji, N., Shukla, P. K., & J, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-03973-x · Cited by 10 · see citing works
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Vanani, I. R., Taghavifard, M. T., & Yalpanian, M. A. (2025) · SN Computer Science · doi:10.1007/s42979-024-03630-6 · Cited by 5 · see citing works
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- Authors
- Shah, A., Varshney, S., & Mehrotra, M.
- Affiliations
- Jamia Millia Islamia (IN)
- Corresponding author
- Shah, Akash
- Venue
- SN Computer Science
- Published
- 2024-04-25
- Volume
- 5
- Issue
- 5
- ISSN
- 2661-8907 (ISSN-L)
- OpenAlex
- W4395463642
- Cited by
- 16
- References
- 44
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 6
- FWCI
- 6.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- SN Computer Science (publishedVersion)
- 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 › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- fake account detection, fake accounts, social networking sites, deep learning, machine learning, class imbalance, SMOTE, data augmentation, LSTM, XGBoost, benchmark dataset, dataset size
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