Annotating reliability to enhance disinformation detection: annotation scheme, resource and evaluation

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


Disinformation is a critical problem in our society. The COVID-19 pandemic and the Russia-Ukraine war have been key events for the spreading of fake news. Assuming that fake news mixes reliable and unreliable information, we propose RUN-AS (Reliable and Unreliable Annotation Scheme), a fine-grained annotation scheme that labels the structural parts and essential content elements of a news item to enable their classification into Reliable and Unreliable. This type of annotation will be used for training systems to automatically classify the reliability of a news item. To this end, RUN dataset in Spanish was built and annotated with RUN-AS. A set of experiments were conducted to validate the annotation scheme. The experiments evidence the validity of the annotation scheme proposed, obtaining the best F1m, i.e., 0.948.

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