Misinformation detection: datasets, models and performance

Abstract

PurposeThis paper aims to understand the characteristics of current misinformation detection studies, including the datasets used by researchers, the computational models or algorithms being developed or applied, and the performance of misinformation detection models or algorithms.Design/methodology/approachWe first identified articles from the Scopus database with inclusion and exclusion criteria. Then a coding scheme was derived from the articles based on research questions. Next, datasets, models, and performance were coded. The paper concluded with answers to research questions and future research directions.FindingsFrom 115 relevant articles published during 2019–2023 on misinformation detection. We found that most studies used previously existing datasets. Twitter (now X) has been the most widely used source for collecting social media misinformation data. The ten most frequently used datasets are identified. Most studies (96.1%) developed or applied machine learning, especially deep learning models. The most advanced current misinformation detection models could achieve pretty high performance. For example, among 104 studies reporting performance with accuracy, 44.2% achieved an accuracy of 0.95 or higher, and 24.0% achieved 0.90–0.94 on accuracy.Research limitations/implicationsOur study only reviewed English articles from 2019–2023 that are included in the Scopus database. Articles that are not included in the Scopus database are not reviewed.Practical implicationsThe high performance of misinformation detection indicates that social media should be able to detect most misinformation if they are willing to do it. However, no system or algorithm could achieve 100% misinformation on performance. Due to the complexity of misinformation, users of social media still need to improve their capabilities of evaluating information on the Internet.Social implicationsThis study provides evidence to policymakers that social media platforms have the capability of detecting most misinformation posted. These platforms are responsible for alerting to suspicious postings with misinformation.Originality/valueThis study identifies datasets, computer models, and performance of models from current misinformation detection research. The findings will help social media companies, computer scientists, and information system designers improve their misinformation detection systems. It will also help students in information science and computer science to study the latest models and algorithms. Information professionals may work with computer scientists to improve datasets used for misinformation detection.