Abstract
Twitter spam has for quite some time been a basic yet troublesome issue. Most kids’ users are not alert on spam issues while accessing the twitter account that will give harm without their acknowledgment. Up until now, scientists have fostered a progression of Machine Learning (ML) based strategies and boycotting procedures to distinguish spamming exercises on Twitter. To recognize spam, it had been ordered into various fields (counterfeit news, counterfeit connections, and substance), spam is identified based on these classes through ML techniques. In this paper, an experimental study is conducted by using ML algorithms to classify whether the tweet is spam or not. This can be done using Bayes theorem, a probabilistic theory is given by Naïve Bayes. Dataset is downloaded from the KAGGLE site which contains the dataset of spam and genuine tweets. The information preprocessed by standard articulations for the expulsion of undesirable information. The element will be changed over into a vector by applying all the arrangement strategies to the information. Term Frequency Inverse Document Frequency (TF-IDF) vectorizer will be utilized for changing over text into vector. Based on the results, The Naïve Bayes (NB) classifier achieved 98% accuracy result on the dataset compared to KNN and SVM.
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Notes
- 1.
0.987 is the accuracy when we calculate it using the equation
$${\rm{Accuracy}} = ({\rm{TP}}+{\rm{TN}})/({\rm{TP}}+{\rm{TN}}+{\rm{FN}}+{\rm{FP}})=0.98746658$$
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Acknowledgements
The authors would like to acknowledge the financial support from Ministry of Higher Education Malaysia (MOHE) under Fundamental Research Grant Scheme (FRGS) (Ref: FRGS/1/2021/ICT08/UTM/02/5) vote R.J130000.7851.5F462 and Research Management Center (RMC) of Universiti Teknologi Malaysia (UTM).
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Alshammari, S., Aljabarti, E., Yusoff, Y. (2023). Protection of Users Kids on Twitter Platform Using Naïve Bayes. In: Yafooz, W.M.S., Al-Aqrabi, H., Al-Dhaqm, A., Emara, A. (eds) Kids Cybersecurity Using Computational Intelligence Techniques. Studies in Computational Intelligence, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-031-21199-7_8
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