| Resumen |
Since the beginning of Covid-19 era in November 2019, the patient growth curve is closely accompa- nied by the growth of fake news. Therefore, developing tools and models for the detection of fake news from real ones in various domains have become more significant than the earlier days. To address the detection of fake news, in this paper, we, team MUCIC, describe the models submitted to ‘Fake News Detection’, a shared task organized by CLEF-2021-CheckThat! Lab. This shared task contains two sub- tasks namely; Fake News Detection of News Articles (Subtask 3A) and Topical Domain Classification of News Articles (Subtask 3B) and both are multi-class text classification tasks. The proposed models have been developed by fine-tuning the three transformer-based language models namely; Roberta, Dis- tilbert, and BERT from HuggingFace using training data and then ensembling them as estimators with majority voting. The proposed models performances evaluated through the evaluation script provided by organizers obtained F1-scores of 0.5309 and 0.8550 for Subtask 3A and Subtask 3B respectively. |
