Fake News Detection: An Image-Based Semi-Automated Method Using Statistic Feature

Abstract

The development of information technology has an impact on the speed with which news spreads in the community. From this news, there are some news that are less credible which can cause unrest to propaganda for certain purposes, or so-called fake news. This study designed a semi-automatic method for detecting fake news based on the headline image of the news. The stages of the proposed method are News Crawling and Representation, Feature Extraction, Similarity Measurement, and Source News Recommendation. The crawling process takes advantage of the Bing search engine API. The image features used are the statistical features for each RGB color component, namely the mean, median, and standard deviation. The results showed that the proposed method was able to provide recommendations to users of news sources from fake news headline images. This detection method is expected to help users detect fake news in the form of false context and manipulated content more efficiently.