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Towards universal methods for fake news detection

Published online by Cambridge University Press:  26 October 2022

Maria Pszona ,
Maria Janicka ,
Grzegorz Wojdyga  and
Aleksander Wawer Open the ORCID record for Aleksander Wawer [Opens in a new window]
Maria Pszona
Affiliation:
Samsung R&D Institute Poland, pl. Europejski 1, 00-844 Warsaw, Poland
Maria Janicka
Affiliation:
Samsung R&D Institute Poland, pl. Europejski 1, 00-844 Warsaw, Poland
Grzegorz Wojdyga
Affiliation:
Institute of Computer Science Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248 Warszawa, Poland
Aleksander Wawer*
Affiliation:
Samsung R&D Institute Poland, pl. Europejski 1, 00-844 Warsaw, Poland Institute of Computer Science Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248 Warszawa, Poland
*
*Corresponding author. E-mail: axw@ipipan.waw.pl
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Abstract

Fake news detection is an emerging topic that has attracted a lot of attention among researchers and in the industry. This paper focuses on fake news detection as a text classification problem: on the basis of five publicly available corpora with documents labeled as true or fake, the task was to automatically distinguish both classes without relying on fact-checking. The aim of our research was to test the feasibility of a universal model: one that produces satisfactory results on all data sets tested in our article. We attempted to do so by training a set of classification models on one collection and testing them on another. As it turned out, this resulted in a sharp performance degradation. Therefore, this paper focuses on finding the most effective approach to utilizing information in a transferable manner. We examined a variety of methods: feature selection, machine learning approaches to data set shift (instance re-weighting and projection-based), and deep learning approaches based on domain transfer. These methods were applied to various feature spaces: linguistic and psycholinguistic, embeddings obtained from the Universal Sentence Encoder, and GloVe embeddings. A detailed analysis showed that some combinations of these methods and selected feature spaces bring significant improvements. When using linguistic data, feature selection yielded the best overall mean improvement (across all train-test pairs) of 4%. Among the domain adaptation methods, the greatest improvement of 3% was achieved by subspace alignment.

Keywords

Fake newsPsycholinguisticsDomain adaptationData set shift
Type
Article
Information
Natural Language Engineering , Volume 29 , Issue 4 , July 2023 , pp. 1004 - 1042
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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