NLP&IR@UNED at CheckThat! 2021: Check-worthiness estimation and fake news detection using transformer models

This article describes the different approaches used by the NLPIR@UNED team in the CLEF2021 CheckThat! Lab to tackle the tasks 1A-English, 1A-Spanish and 3A-English. The goal of Task 1A in English is to determine which tweets within a set of COVID-19 related tweets are worth checking. Task 1A in Spanish is similar but in this case the tweets are related to political issues in Spain. In both tasks, transformer models have been used to identify check-worthy tweets, obtaining the first place in the task in English and the fourth place in the task in Spanish. Task 3A is focused on determining the veracity of a news article. It is a multi-class classification problem with four possible values: true, partially false, false, and other. For this task we have used two different approaches: a gradient-boosting classifier with TF-IDF and LIWC features, and a transformer model fed with the first tokens of each news article. We got the fourth place out of 25 participants in this task. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).