@inproceedings{hamalainen-etal-2021-never,
title = "Never guess what {I} heard... Rumor Detection in {F}innish News: a Dataset and a Baseline",
author = {H{\"a}m{\"a}l{\"a}inen, Mika and
Alnajjar, Khalid and
Partanen, Niko and
Rueter, Jack},
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4if-1.6",
doi = "10.18653/v1/2021.nlp4if-1.6",
pages = "39--44",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline
%A Hämäläinen, Mika
%A Alnajjar, Khalid
%A Partanen, Niko
%A Rueter, Jack
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F hamalainen-etal-2021-never
%X 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.
%R 10.18653/v1/2021.nlp4if-1.6
%U https://aclanthology.org/2021.nlp4if-1.6
%U https://doi.org/10.18653/v1/2021.nlp4if-1.6
%P 39-44
Markdown (Informal)
[Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline](https://aclanthology.org/2021.nlp4if-1.6) (Hämäläinen et al., NLP4IF 2021)
ACL