False information detection in online content and its role in decision making: a systematic literature review

Abstract

This work presents a review of detecting false information and its role in decision making spread across online content. The authenticity of information is an emerging issue that affects society and individuals and has a negative impact on people’s decision-making capabilities. The purpose is to understand how different techniques can be used to address the challenge. The approach used for the identification of published articles between 2014 and 2018 is the systematic literature review in which 30 papers were identified and the relevant articles were selected by applying inclusion–exclusion criteria. This review classifies the false information, spreading on social media, into four types. Furthermore, we describe four deep learning and eight machine learning techniques for false information detection. The outcomes of this review will provide the researchers with an insight into the different types of false information, associated detection techniques, and the relationship between false information and decision making. In the field of false information detection, previous studies provided a review of the literature. However, we conducted a systematic literature review by providing specific answers to the proposed research questions. Therefore, our contribution is novel to the field because this type of study is not performed previously.