RUN-AS: a novel approach to annotate news reliability for disinformation detection

Alba Bonet-Jover, Robiert Sepúlveda-Torres, Estela Saquete, Patricio Martínez-Barco, Mario Nieto-Pérez

Abstract: The development of the internet and digital technologies has inadvertently facilitated the huge disinformation problem that faces society nowadays. This phenomenon impacts ideologies, politics and public health. The 2016 US presidential elections, the Brexit referendum, the COVID-19 pandemic and the Russia-Ukraine war have been ideal scenarios for the spreading of fake news and hoaxes, due to the massive dissemination of information. Assuming that fake news mixes reliable and unreliable information, we propose RUN-AS (Reliable and Unreliable Annotation Scheme), a fine-grained annotation scheme that enables the labelling of the structural parts and essential content elements of a news item and their classification into Reliable and Unreliable. This annotation proposal aims to detect disinformation patterns in text and to classify the global reliability of news. To this end, a dataset in Spanish was built and manually annotated with RUN-AS and several experiments using this dataset were conducted to validate the annotation scheme by using Machine Learning (ML) and Deep Learning (DL) algorithms. The experiments evidence the validity of the annotation scheme proposed, obtaining the best \(\textbf F_\textbf 1\textbf m\), 0.948, with the Decision Tree algorithm.