Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline
This addresses rumor detection for Finnish language news, but it is incremental as it applies existing methods to a new dataset.
The study tackled rumor detection in Finnish news headlines by creating a new dataset and evaluating LSTM and BERT models, with a fine-tuned FinBERT achieving 94.3% overall accuracy and 96.0% rumor label accuracy.
This study presents a new dataset on rumor detection in Finnish language news headlines. We have evaluated two different LSTM based models and two different BERT models, and have found very significant differences in the results. A fine-tuned FinBERT reaches the best overall accuracy of 94.3% and rumor label accuracy of 96.0% of the time. However, a model fine-tuned on Multilingual BERT reaches the best factual label accuracy of 97.2%. Our results suggest that the performance difference is due to a difference in the original training data. Furthermore, we find that a regular LSTM model works better than one trained with a pretrained word2vec model. These findings suggest that more work needs to be done for pretrained models in Finnish language as they have been trained on small and biased corpora.