Implementation of a Priority Queue to Optimize Resources during Manual Verification of Fake News

Abstract

Combating fake news on social media is a critical challenge in today's digital age, especially when misinformation is spread regarding vital matters such as the Covid-19 pandemic. Manual verification of all content is infeasible; hence, Artificial Intelligence is used to classify fake news. Our ensemble model uses multiple Natural Language Processing techniques to analyze the truthfulness of the text in tweets. We create custom parameters that analyze the consistency and truthfulness of domains contained in hyperlinked URLs. We then combine these parameters with the results of our deep learning models to achieve classification with greater than 99% accuracy. We have proposed a novel method to calculate a custom coefficient, the Combined Metric of Prediction Uncertainty (CMPU), which is a measure of how uncertain the model is of its classification of a given tweet. Using CMPU, we have proposed the creation of a priority queue following which the tweets classified with the lowest certainty can be manually verified. By manually verifying 3.93% of tweets, we were able to improve the accuracy from 99.02% to 99.77%.