Hybrid Machine-Crowd Approach for Fake News Detection

Abstract

The rapid growth of fake news, especially in social media has become a challenging problem that has negative social impacts on a global scale. In contrast to fake news which intend to deceive and manipulate the reader, satirical stories are designed to entertain the reader by ridiculing or criticizing a social figure. Due to its serious threats of misleading information, researchers, governments, journalists and fact-checking volunteers are working together to address the fake news issue and increase the accountability of digital media. The automatic fake news detection systems enable identification of deceptive news. Low accuracy remains the main drawback of these systems. The automatic detection using only news' content is a technically challenging task as the language used in these articles is made to bypass the fake news detectors. This becomes even more complicated when the task is to differentiate the satirical stories from fake news. On the other side, human cognitive skills have shown to overperform machine-based systems when it comes to such tasks. In this paper, we address the fake news and satire detection by proposing a method that uses a hybrid machine-crowd approach for detection of potentially deceptive news. This system combines the human factor with the machine learning approach and a decision-making model that estimates the classification confidence of algorithms and decides whether the task needs human input or not. Our approach achieves reasonably higher accuracy compared to the reported baseline results, in exchange of cost and latency of using the crowdsourcing service.