Automated Identification of Disaster News For Crisis Management Using Machine Learning

TL;DR

This paper develops machine learning models to accurately distinguish between legitimate and fake news articles about Typhoon Rai, aiding crisis management efforts by filtering misinformation.

Contribution

It introduces a combined machine learning approach using Bag of Words and TF-IDF with multiple algorithms to improve fake news detection accuracy.

Findings

Combined BOW model achieved 91.07% accuracy

Combined TF-IDF model achieved 91.18% accuracy

Models demonstrated high recall rates for fake news detection

Abstract

A lot of news sources picked up on Typhoon Rai (also known locally as Typhoon Odette), along with fake news outlets. The study honed in on the issue, to create a model that can identify between legitimate and illegitimate news articles. With this in mind, we chose the following machine learning algorithms in our development: Logistic Regression, Random Forest and Multinomial Naive Bayes. Bag of Words, TF-IDF and Lemmatization were implemented in the Model. Gathering 160 datasets from legitimate and illegitimate sources, the machine learning was trained and tested. By combining all the machine learning techniques, the Combined BOW model was able to reach an accuracy of 91.07%, precision of 88.33%, recall of 94.64%, and F1 score of 91.38% and Combined TF-IDF model was able to reach an accuracy of 91.18%, precision of 86.89%, recall of 94.64%, and F1 score of 90.60%.