Survey Paper on Automatic Detection of Fake Profile Using Machine Learning on Instagram

Authors

  • Er. Pranay Meshram  Assistant Professor, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Rutika Bhambulkar  BE Scholar, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Puja Pokale  BE Scholar, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Komal Kharbikar  BE Scholar, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India
  • Anushree Awachat  BE Scholar, Department of Computer Science and Engineering, Priyadarshini J. L. College of Engineering, Nagpur, Maharashtra, India

DOI:

https://doi.org//10.32628/IJSRSET218313

Keywords:

Fakeprofile, Detection, Machine Learning, Social Media, Instagram, Internet

Abstract

With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.

References

  1. Guo, G., Zhu, Y., Yu, R., Chu, W.C.C., Ma, D. (2020). A privacy-preserving framework with self- governance and permission delegation in online social networks.IEEE Access,8:157116-157129. https://doi.org/10.1109/ACCESS.2020.3016041
  2. Boididou, C., Middleton, S.E., Jin, Z., Papadopoulos, S., Dang-Nguyen, D.T., Boato, G., Kompatsiaris, Y. (2018). Verifying information with multimedia content on twitter. Multimedia Tools and Applications, 77(12): 15545-15571. https://doi.org/10.1007/s11042-017-5132-9
  3. Alqatawna, J., Madain, A., Al-Zoubi, A., Al-Sayyed,R. (2017). Online social networks security: Threats, attacks, and future directions. In Social Media Shaping ePublishing and Academia,pp.121132. https://doi.org/10.1007/978-3-319-55354-210
  4. Lorincz, L., Koltai, J., Gy?r, A.F., Takacs, K. (2019). Collapse of an online social network: Burning social capital to create it? Social Networks,57:43-53. https://doi.org/10.1016/j.socnet.2018.11.004
  5. Arora, A., Bansal, S., Kandpal, C., Aswani, R., Dwivedi, Y. (2019). Measuring social media influencer index-insights from Facebook, Twitter and Instagram. Journal Of Retailing and Consumer Services, 49: 86-101. https://doi.org/10.1016/j.jretconser.2019.03.012
  6. Boerman, S.C. (2020). The effects of the standardized Instagram disclosure for micro-and meso- influencers. Computers in Human Behavior, 103:199-207. https://doi.org/10.1016/j.chb.2019.09.015
  7. Yang, C., Harkreader, R., Gu, G. (2013). Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Transactions on Information Forensics and Security,8(8):1280-1293. https://doi.org/10.1109/TIFS.2013.2267732
  8. Han, Y., Fang, B., Jia, Y. (2014). Predicting the topic influence trends in social media with multiple models.Neurocomputing,144:463-470. https://doi.org/10.1016/j.neucom.2014.03.054
  9. Jr Barbon, S., Igawa, R.A., Zarpelao, B.B. (2017). Authorship verification applied to detection of compromised accounts on online social networks. Multimedia Tools and Applications, 76(3):3213-3233. https://doi.org/10.1007/s11042-016-3899-8
  10. Blair, S.J., Bi, Y., Mulvenna, M.D. (2020). Aggregated topic models for increasing social media topic coherence. Applied Intelligence, 50(1): 138-156. https://doi.org/10.1007/s10489-019-01438-z

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Er. Pranay Meshram, Rutika Bhambulkar, Puja Pokale, Komal Kharbikar, Anushree Awachat, " Survey Paper on Automatic Detection of Fake Profile Using Machine Learning on Instagram, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 3, pp.46-50, May-June-2021. Available at doi : https://doi.org/10.32628/IJSRSET218313