Tool to Detect Fake Accounts in Twitter

Madhwaraj Kango Gopal; V. Asha; Binju Saju; R. Anju Shree; R. Aishwarya

doi:10.2991/978-94-6463-250-7_29

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Tool to Detect Fake Accounts in Twitter

A Case Study Using Machine Learning Algorthims

Authors

Madhwaraj Kango Gopal¹^{, *}, V. Asha², Binju Saju³, R. Anju Shree⁴, R. Aishwarya⁵

¹Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India

²Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India

³Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India

⁴Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India

⁵Department of MCA, New Horizon College of Engineering(VTU), Bangalore, India

^*Corresponding author. Email: dr.madhwaraj@newhorizonindia.edu

Corresponding Author

Madhwaraj Kango Gopal

Available Online 17 October 2023.

DOI: 10.2991/978-94-6463-250-7_29 How to use a DOI?
Keywords: — Fake Profiles; Twitter datasets; social media; supervised
Abstract: On the internet social media platforms are growing in popularity, and that popularity has raised increasing questions about security and privacy. Users of social networks face serious security risks due to fake and copied profiles. One major issue is the cloning of user profiles, when duplicate profiles are created using stolen user data and then utilized to harm the original profile owner. Threats like phishing, stalking, spamming, and others are also used to accomplish a variety of goals. A fake profile is one that is made on a social networking site using the name of an organization or person that does not exist and participates in destructive activities. In this study, a new tool is created that uses machine learning methods to verify user identification. People who utilize fake accounts may be recognized as having fake profiles in one of three ways: the number of abuse reports, daily comments, or rejected friend requests. Data from Twitter was used in a case study. The Random Forest algorithm and the Support vector machine approach offered a greater projected accuracy when detecting whether a user was a fraudulent or genuine user compared to other machine learning methods.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
Series: Advances in Computer Science Research
Publication Date: 17 October 2023
ISBN: 10.2991/978-94-6463-250-7_29
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-250-7_29 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Madhwaraj Kango Gopal
AU  - V. Asha
AU  - Binju Saju
AU  - R. Anju Shree
AU  - R. Aishwarya
PY  - 2023
DA  - 2023/10/17
TI  - Tool to Detect Fake Accounts in Twitter
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 165
EP  - 171
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_29
DO  - 10.2991/978-94-6463-250-7_29
ID  - Gopal2023
ER  -

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