Fake news detection: A machine learning approach

Yeoh, Dennis Guan Lee (2021) Fake news detection: A machine learning approach. Final Year Project, UTAR.

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Abstract

The spread of fake news is nothing new in the current day and age, there is a lot of news being spread in Malaysia related to the Covid-19 pandemic, some of which may not be true. Websites like Sebenarnya.my and Malaysiakini can be used to check whether a news headline is true, however this is a manual and tedious process. Furthermore, there are currently no datasets available that specifically focus on Covid-19 headlines in Malaysia. This project aims to reduce the spread of fake news in Malaysia by developing a web application that can ease and automate the news verification process. The aim of this project was achieved through several objectives. Firstly, a small dataset that is specific to Covid-19 headlines in Malaysia was collected. Next, a competent classification model for determining whether a headline regarding Covid-19 in Malaysia is true, fake, or unsure was trained by using the dataset collected. Finally, a web application was developed to deploy the trained model. The originality of this project lies in the fact that the dataset used to train the model was self-collected. The main contribution of this project on the other hand is the web application that deviates from the usual data verification process which is often done manually. The data collected for the creation of the dataset is obtained in the form of tweets using a Twitter API. These tweets are then labelled as Real, Fake and Unsure according to the sources that posted the tweets. The tweet data then undergoes several pre-processing steps in order to prepare it for model training. Once the dataset was created, several machine learning algorithms were used to train several different models. These models were evaluated in order to pick one to be deployed to the web application. The final model chosen to be deployed was a model trained using a Multinomial Naïve Bayes algorithm.

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