A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

Abstract

This paper aims to analyse the online social network for reconnaissance of people for finding their potentiality. The work considers one of the famous social networking sites, Twitter, where people express their thoughts and ideas, through which the people in the site knowingly or unknowingly reveal the information about themselves such as personal interests, likes and dislikes. The Machine Learning technique facilitates the work to mine the tweet data of a person to get his/her 360-degree profiling. This profiling is helpful to identify the personality type of a person, which is essential for the Government to identify the people involved in spreading the fake news in Twitter.

On the Effect of Intelligent Neurally Processed Features on COVID–19 Fake News Detection Performance

“…The authors of [33] utilized Machine Learning algorithms to address the issue of digit recognition. To detect the people propagating fake news on Twitter, [34] mined the users' tweets to obtain their 360-degree profiling using Machine Learning techniques, namely, Logistic Regression, Random Forest and Decision Tree. Some of the Deep Learning models applied for the classification of fake news are Recurrent Neural Network (RNN), LSTM [10], Gated Recurrent Unit (GRU), CNN and Bidirectional Long Short Term Memory (BiLSTM).…”