Threat Detector for Social Media Using Text Analysis

Authors

  • Saidul Haq Sadi Department of Information Systems, Kulliyyah of ICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Md Rubel Hossain Pk Department of Information Systems, Kulliyyah of ICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Akram M Zeki Department of Information Systems, Kulliyyah of ICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia

Abstract

The scam is one of the most important security threats among social media users. It is required to detect scams not only to protect social user's data that is stored online but also to secure the social network. Besides, machine learning techniques are becoming more popular in the text analysis sector. To fraud detection, the most used supervised machine learning techniques areNaïve Bayes (NB)and Support vector machine (SVM). In this project, a machine learning model is developed for detecting threats from Twitter tweets. Accordingly, the Naïve Bayes classifier and flask microweb framework were used to build the model by using the python programming language. The model provided 91% accuracy in detecting tweet scam threats. This finding will benefit the social network users to be aware of threats as well as social media network providers to enhance their security system

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Published

2021-07-16

How to Cite

Sadi, S. H., Pk, M. R. H., & Zeki, A. M. (2021). Threat Detector for Social Media Using Text Analysis. International Journal on Perceptive and Cognitive Computing, 7(1), 113–117. Retrieved from https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/234

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Articles