| Title | Spam Detection in Social Media using Artificial Neural Network Algorithm and comparing Accuracy with Support Vector Machine Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Svadasu, Grandhi, Adimoolam, M. |
| Conference Name | 2022 International Conference on Business Analytics for Technology and Security (ICBATS) |
| Keywords | artificial neural network, Artificial neural networks, Classification algorithms, Human Behavior, learning (artificial intelligence), machine learning, Metrics, Novel spam detection, Prediction algorithms, pubcrawl, Scalability, social media, social networking (online), spam detection, support vector machine, Support vector machines, Training |
| Abstract | Aim: To bring off the spam detection in social media using Support Vector Machine (SVM) algorithm and compare accuracy with Artificial Neural Network (ANN) algorithm sample size of dataset is 5489, Initially the dataset contains several messages which includes spam and ham messages 80% messages are taken as training and 20% of messages are taken as testing. Materials and Methods: Classification was performed by KNN algorithm (N=10) for spam detection in social media and the accuracy was compared with SVM algorithm (N=10) with G power 80% and alpha value 0.05. Results: The value obtained in terms of accuracy was identified by ANN algorithm (98.2%) and for SVM algorithm (96.2%) with significant value 0.749. Conclusion: The accuracy of detecting spam using the ANN algorithm appears to be slightly better than the SVM algorithm. |
| DOI | 10.1109/ICBATS54253.2022.9758927 |
| Citation Key | svadasu_spam_2022 |
Spam Detection in Social Media using Artificial Neural Network Algorithm and comparing Accuracy with Support Vector Machine Algorithm