Adaptive Goal Function of Ant Colony Optimization in Fake News Detection

Abstract

Currently, there is a very rapid growth of information published on the Internet, both on social media and news sites. However, a serious problem is a disinformation in the form of fake news. Due to the rapid spread of information on the Internet, it is very important to be able to quickly identify true and fake news. The solution to this problem can be an initial analysis of news by its title and quick selection of true or fake news. Additionally, the possibility of balancing precision and recall as the quality of classification measures could allow for better news selection. In this paper, we propose the use of the adaptive goal function of ant colony optimization algorithms in fake news detection. The goal of this solution is to increase recall or precision of the selected class – in this case fake or true news. We use natural language processing (NLP) to describe the title of the news. In addition, a constrained term matrix is used. The choice of titles alone and the restriction of the words analyzed are related to speeding up the initial classification. Eventually, we present an analysis of a real dataset and classification results (detailing recall and precision) of news using the adaptive goal function of the ACDT algorithm.