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Evaluating the Performance of Machine Learning Classifiers for Detecting Twitter Spam
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 10 |
| Year of Publication: 2023 |
| Authors: Dipalee B. Borse, Swati K. Borse, Vijaya Ahire |
10.5120/ijca2023922766
|
Dipalee B. Borse, Swati K. Borse, Vijaya Ahire . Evaluating the Performance of Machine Learning Classifiers for Detecting Twitter Spam. International Journal of Computer Applications. 185, 10 ( May 2023), 12-17. DOI=10.5120/ijca2023922766
Abstract
The usage of social networking sites is rising rapidly every day. The popularity of twitter as a microblogging site is huge in normal users as well as illegitimate users. The people with wrong intentions use twitter to spread spam posts which results in phishing, monetary loss, un-useful or noisy data on social media, stealing personal information etc. It becomes extremely important to stop spamming activities. In this paper six machine learning classifiers, which are Logistic regression & Support Vector machine (linear models) and Random forest, K- Nearest Neighbor, Decision tree and Naive Bayes, (nonlinear models), have been implemented on existing data and compared the performance using different parameters such as accuracy, F1-score, recall, precision, f-measure. Among the six classifiers random forest has shown better accuracy followed by K-nearest neighbor classifier for large continuous dataset than small or random dataset. The accuracy is increased from 3% to 13% for large continuous data. Also False positive ratio of random forest and K-nearest neighbor algorithm 0.001 and 0.005 respectively which is much lesser than other algorithms. With lowest accuracy and highest FPR Naive Bayes algorithm performed worst for large datasets.
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