Fake Profile Detection Using Machine Learning Techniques
Open accessAbstract
Our lives are significantly impacted by social media platforms such as Facebook, Twitter, Instagram, LinkedIn, and others. People are actively participating in it the world over. However, it also has to deal with the issue of bogus profiles. False accounts are frequently created by humans, bots, or computers. They are used to disseminate rumors and engage in illicit activities like identity theft and phishing. So, in this project, the author’ll talk about a detection model that uses a variety of machine learning techniques to distinguish between fake and real Twitter profiles based on attributes like follower and friend counts, status updates, and more. The author used the dataset of Twitter profiles, separating real accounts into TFP and E13 and false accounts into INT, TWT, and FSF. Here, the author discusses LSTM, XG Boost, Random Forest, and Neural Networks. The key characteristics are chosen to assess a social media profile’s authenticity. Hyperparameters and the architecture are also covered. Finally, results are produced after training the models. The output is therefore 0 for genuine profiles and 1 for false profiles. When a phony profile is discovered, it can be disabled or destroyed so that cyber security problems can be prevented. Python and the necessary libraries, such as Sklearn, Numpy, and Pandas, are used for implementation. At the end of this study, the author will come to the conclusion that XG Boost is the best machine learning technique for finding fake profiles.
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Fake accounts contribute to spreading false information, illegal activities such as identity theft and phishing, and rumours, which may create social rigidities and divergences.
“There are several concerns such as spreading false information, engaging in illegal activities such as character burglary and phishing, and rumours, which may create rigidities and divergences in society7.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [7]
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Cites
Deep neural networks for bot detection
Kudugunta, S., & Ferrara, E. (2018) · Information Sciences · doi:10.1016/j.ins.2018.08.019 · Cited by 506 · 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
Detecting Fake Accounts on Social Media
Khaled, S., El-Tazi, N., & Mokhtar, H. M. O. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8621913 · Cited by 131 · 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
Detecting Fake Accounts in Online Social Networks at the Time of Registrations
Yuan, D., Miao, Y., Gong, N. Z., Yang, Z., Li, Q. L., Song, D. X., Wang, Q., & Liang, X. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363198 · Cited by 75 · 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
Automatic Detection of Fake Profile Using Machine Learning on Instagram
Meshram, E. P., Bhambulkar, R., Pokale, P., Kharbikar, K., & Awachat, A. (2021) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst218330 · Cited by 36 · see citing works
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Sarker, A., Chakraborty, P., Sha, S. M. S., Khatun, M., Hasan, M. R., & Banerjee, K. (2020) · Journal of Computer and Communications · doi:10.4236/jcc.2020.87005 · Cited by 30 · see citing works
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Self-citationFake Social Media Profile Detection
Joshi, U. D., Vanshika, Singh, A. P., Pahuja, T. R., Naval, S., & Singal, G. (2021) · doi:10.1002/9781119769262.ch11 · Cited by 19 · see citing works
Use of Artificial Neural Networks to Identify Fake Profiles
Hajdu, G., Minoso, Y., Lopez, R., Acosta, M. H., & Elleithy, A. (2019) · IEEE Long Island Systems, Applications and Technology Conference (LISAT) · doi:10.1109/lisat.2019.8817330 · Cited by 16 · see citing works
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Sultana, M., Chakraborty, P., & Choudhury, T. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-4284-5_24 · Cited by 14 · see citing works
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Muzammel, C. S., Chakraborty, P., Akram, M. N., Ahammad, K., & Mohibullah, M. (2020) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.c8893.029420 · Cited by 13 · see citing works
Self-citationAssessment of Eyeball Movement and Head Movement Detection Based on Reading
Sayeed, S., Sultana, F., Chakraborty, P., & Yousuf, M. A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-6966-5_10 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Ahammad, K., Shawon, J.A.B., Chakraborty, P., Islam, M.J. and Islam, S. (2021) Recognizing Bengali Sign Language Gestures for Digits in Real Time using Convo- lutional Neural Network. International Journal of Computer Science and Informa- tion Security (IJCSIS), 19, 11-19.
- Reddy, S.D.P. (2019) Fake Profile Identification Using Machine Learning. Interna- tional Research Journal of Engineering and Technology (IRJET), 6, 1145-1150.
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Subair, N. K., Nizar, N., & Gopinathan, D. (2025) · AIP conference proceedings · doi:10.1063/5.0248446 · Cited by 1 · see citing works
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GUPTA, M., Rajkumar, S., ELANGO, K., & SUBRAMANIAM, S. K. (2023) · Research Square · doi:10.21203/rs.3.rs-3215501/v1
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Bishtawi, T., Alnagdawi, M., & Alzu’bi, R. (2026) · AIP conference proceedings · doi:10.1063/5.0318241
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- Authors
- Chakraborty, P., Shazan, M. M., Nahid, M., Ahmed, M. K., & Talukder, P. C.
- Affiliations
- Comilla University (BD)
- Corresponding author
- Chakraborty, Partha
- Venue
- Journal of Computer and Communications
- Publisher
- Scientific Research Publishing, Inc.
- Published
- 2022
- Volume
- 10
- Issue
- 10
- Pages
- 74-87
- ISSN
- 2327-5219 (ISSN-L)
- 2327-5227
- OpenAlex
- W4312750304
- Cited by
- 32
- References
- 22
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 8 · 2023: 4
- FWCI
- 6.68
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Computer and Communications (publishedVersion, cc-by)
- 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 profile detection, social bots, Twitter accounts, bot detection, XGBoost, LSTM, random forest, artificial neural networks, status updates, cybersecurity
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