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Fake Profile Detection and Stalking Prediction on Facebook

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1380)

Abstract

The increasing popularity and demand of social media has resulted in connecting people across the globe in a better way. The use of social media platforms to express their views and showcase their day-to-day life is increasing gradually. The activities related to social, business, entertainment and information are being exchanged regularly in social networking. In case of Facebook, there are approximately 1.5 billion users and this count is increasing daily. More than 10 million likes and shares are performed daily on Facebook. Many other networks, like LinkedIn, Instagram, Twitter, Snapchat, etc., are also growing exponentially. But, with all the advancements and growth, several problems are also introduced. Facebook has its own benefits to people but at the same time Facebook is being targeted for many malicious activities such as creating fake profiles to stalk people, online impersonation, etc., which can harm the reputation and invade privacy in online social platform. One of the challenging problems in social network security is to recognize the fake profiles. This has resulted in need of cybersecurity measures and applications to prevent people from cyberbullying such as stalking from fake profiles. In this paper, a framework to classify a Facebook profile as genuine or fake using machine learning techniques is proposed and the same framework will be used for the prediction of stalking.

Keywords

  • Facebook
  • Fake profile
  • stalking
  • Facebook Graph API

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  • DOI: 10.1007/978-981-16-1740-9_2
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Acknowledgements

We would like to thank Rudrani Wankhade, Student in IT Department, NITK for helping us in this work.

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Swathi, M., Anoop, A., Rudra, B. (2022). Fake Profile Detection and Stalking Prediction on Facebook. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_2

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