Comparative analysis of identifying accuracy of online misinformation of Covid-19 using SVM algorithm with logistic regression.

Aim: To predict the accuracy percentage of misinformation about COVID-19 using SVM algorithm. Materials and methods: Support Vector Machine (SVM) with sample size=20 and Logistic Regression with sample size=20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and Discussion: SVM has significantly better accuracy (94.48%) compared to Logistic Regression accuracy (91.07%). SVM performs significantly better than the Logistic Regression with (p=0.024) (p<0.05). Conclusion: SVM with Novel Poly kernel helps in predicting with more accuracy the percentage of misinformation about COVID-19 [ABSTRACT FROM AUTHOR]