2023
doi: 10.12720/jait.14.2.178-184
Comparison of Machine Learning Algorithms for Spam Detection
Azeema Sadia1,*
, , ,
- 1Bahria University, Dept. of Computer Science, Karachi, Pakistan
- 2Sir Syed University of Engineering & Technology, Dept. of Software Engineering, Karachi, Pakistan
- 3Bahria University, Dept. of Software Engineering, Karachi, Pakistan
- Manuscript receivedAugust 29, 2022
- revisedOctober 13, 2022
- acceptedOctober 31, 2022
- publishedMarch 8, 2023
Abstract
The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.
Keywords
- spam detection
- Naive Bayes
- machine learning
- data analysis
- artificial analysis
How to Cite
Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, and Ammarah Khalid, "Comparison of Machine Learning Algorithms for Spam Detection," Journal of Advances in Information Technology, vol. 14, no. 2, pp. 178-184, 2023. https://doi.org/10.12720/jait.14.2.178-184
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
