2 code implementations • WS (NoDaLiDa) 2019 • Anders Edelbo Lillie, Emil Refsgaard Middelboe, Leon Derczynski
In our experiments, monolinugal scores reach stance-based veracity accuracy of 0. 83 (F1 0. 68); applying the model across languages predicts veracity of claims with an accuracy of 0. 82 (F1 0. 67).
1 code implementation • 2 Jul 2019 • Anders Edelbo Lillie, Emil Refsgaard Middelboe
Furthermore, experiments show that stance labels can be used across languages and platforms with a HMM to predict the veracity of rumours, achieving an accuracy of 0. 82 and F1 score of 0. 67.
no code implementations • 29 Jun 2019 • Anders Edelbo Lillie, Emil Refsgaard Middelboe
This paper surveys and presents recent academic work carried out within the field of stance classification and fake news detection.