# Linguistic Cues of Deception in a Multilingual April Fools' Day Context

arXiv: https://arxiv.org/abs/2111.03913 · HTML: https://mlsanity.com/paper/2111.03913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [GREEK-BERT: The Greeks visiting Sesame Street](https://mlsanity.com/paper/2008.12014/) (2020)
- [Deception detection in text and its relation to the cultural dimension of individualism/collectivism](https://mlsanity.com/paper/2105.12530/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Roadmap for Multilingual, Multimodal Domain Independent Deception Detection](https://mlsanity.com/paper/2405.03920/) (2024)

## Abstract (arXiv)

In this work we consider the collection of deceptive April Fools' Day(AFD) news articles as a useful addition in existing datasets for deception detection tasks. Such collections have an established ground truth and are relatively easy to construct across languages. As a result, we introduce a corpus that includes diachronic AFD and normal articles from Greek newspapers and news websites. On top of that, we build a rich linguistic feature set, and analyze and compare its deception cues with the only AFD collection currently available, which is in English. Following a current research thread, we also discuss the individualism/collectivism dimension in deception with respect to these two datasets. Lastly, we build classifiers by testing various monolingual and crosslingual settings. The results showcase that AFD datasets can be helpful in deception detection studies, and are in alignment with the observations of other deception detection works.