# Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline

arXiv: https://arxiv.org/abs/2106.03389 · HTML: https://mlsanity.com/paper/2106.03389/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [Endangered Languages are not Low-Resourced!](https://mlsanity.com/paper/2103.09567/) (2021)
- [A Finnish News Corpus for Named Entity Recognition](https://mlsanity.com/paper/1908.04212/) (2019)
- [A Multi-Modal Method for Satire Detection using Textual and Visual Cues](https://mlsanity.com/paper/2010.06671/) (2020)
- [Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues](https://mlsanity.com/paper/1910.01160/) (2019)
- [When Word Embeddings Become Endangered](https://mlsanity.com/paper/2103.13275/) (2021)
- [Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor](https://mlsanity.com/paper/2011.00259/) (2020)

## Built on by (most cited first, 4 of 4)

- [Detecting Depression in Thai Blog Posts: a Dataset and a Baseline](https://mlsanity.com/paper/2111.04574/) (2021)
- [Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP](https://mlsanity.com/paper/2207.04453/) (2022)
- [Automatic Generation of Factual News Headlines in Finnish](https://mlsanity.com/paper/2212.02170/) (2022)
- [Harnessing Multilingual Resources to Question Answering in Arabic](https://mlsanity.com/paper/2205.08024/) (2022)

## Abstract (arXiv)

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.