Comparative Analysis of Various Machine Learning Based Techniques for Predicting the Virality of Tweets

Abstract

Social media has become more popular, and people tend to read the news more often from it than traditional media. But all the information that is posted on the social media platform might not go viral. In this paper, we have analyzed the data from one of the social media platforms, Twitter, and established a few reasons for the virality of tweets. Along with it, given the tweet information and user details to the trained model, we could predict whether the tweets go viral or not. For this, we used multiple architectures from classical machine learning like Random Forest, XGBoost and Lightgbm and Convolutions from Deep Learning and got the highest accuracy using the Lightgbm model. The results show that using both text and image data combined provides better results when compared with using only text or images (unimodal data). The data used is from the competition with full user details, tweet information, and tweet text and image.