Fake News Detection on Social Media Using Deep Learning and Machine Learning

Abstract

Purpose:
The research question to be addressed in this paper is centred on fake news, which is being spread through social media and particularly Twitter. The purpose is to create a model which will be used to classify the database of Twitter for fake news and predict the accuracy. The purpose is to maintain a high level of online media and social networking space.
Methodology:
In order to attain this goal, the researcher created a way to measure the accuracy of news messages with emphasis on data which originated from tweets. This study compares the performance of five distinct classification algorithms: The selected algorithms include Logistic Regression, Support Vector Machine (SVM), Naive Bayes, and Recurrent Neural Network. These techniques were used both for model training and for model calibration as to their abilities to predict.The objective of the study is to develop a method to track and categorize fakenews in a given corpus of tweets. In order to achieve this the approach aims at predicting and enhancing the accuracy of detection in an attempt to contribute to an improved credibility within social media and online networking.
Findings:
In the present work, it is revealed that the two classification techniques, namely Support Vector Machine (SVM) and Naive Bayes classifiers are most effective to detect fake news in twitter. A model using these kind of algorithms was inferred and on a set containing tweets it was proved that it had better performance in the classification. This goes a long way in enhancing the credibility of these methods in the identification of the misleading contents on popular social medial. The goal is to help to prevent a doubtful and dangerous online media and social networking presence by using a reliable method of recognizing fake news with high efficiency.
Recommendations:
In light of the research findings, the following recommendations are made; The automation of the identification of fake news on Twitter should be done using Support Vector Machine (SVM) and Naive Bayes classifier algorithms. These strategies have been seen to work well in detecting false news stories. It is possible that incorporating such models into social media applications may greatly enhance the reliability of information transfer and protect consumers from fake news. In addition, there is increased emphasis on the research and development processes as crucial in the production of the product.