Leveraging Open Access publishing to fight fake news
Creators: name: Massip, Sylvain
name: Letaillieur, Charles
Publisher: name: Zenodo
Year: 2020
Subjects: Open Access
Text-mining
Fake News
Fact-checking
Word2Vec

description: Since the very first experiences in Open Access publishing at the end of 20 th century,<br> (arXiv and PLOS, two pioneers of open access distribution of academic articles were<br> created in 1991 and 2001, respectively), Open Access has developed tremendously. Today, a significant fraction of research is published open access. Evaluation estimates<br> it to be as high as 28% [Piwowar, 2018] and it occupies an ever-growing position in the<br> scientific debate with the adoption, in 2018 of the plan S which creates an European<br> level mandate for Open Access. In addition to being ethically desirable per se, there are many academic, economic and<br> societal arguments in favor of open access. These arguments, based on an improvement<br> of the exploitation and reuse of research results, are well described theoretically in the<br> litterature [Tennant, 2017]. Nevertheless, the practical demonstration of the use of Open<br> Access outside research communities are not common, and we have not many reports of<br> these. The objective of our project is to illustrate the possible uses of Open Access<br> outside of academia. In this study, we will examine how open access combined with the right machine<br> learning tools can help fight fake news. Natural Language processing has been revolutionized these last years, by the use of<br> neural networks based language models such as word2Vec [Mikolov, 2013] and Bert<br> [Devlin, 2018]. By building space representation of the words and concepts used in texts, these models<br> are able to take into account the meanings of studied texts. These methods have been<br> shown to be of use to create knowledge bases from corpus of texts [Petroni, 2019] in a<br> unsupervised manner. More specifically, [Tshitoyan, 2019] has shown that these<br> methods, applied to a scientific corpus in an unsupervised manner, were able to retrieve<br> the links between concepts that exists in the texts. This study will investigate how these principles will be used to build a text-mining<br> pipeline that indicates whether a scientific claim is backed by the scientific literature or<br> not. In this exploratory phase, the following methods will be applied: data from Euro Pubmed Central database will be used to train a Word2Vec model. claims will be restricted to health-related questions of the pattern “Does X cure/cause/prevent Y?”. Claims will then be classified by exploring the links between X, Y and the concept of cure / cause / prevent as learned in the language model. The pipeline will be evaluated with claims taken from expert-based scientific<br> fact-checking network such as metafact.io or sciencefeedback.co. By validating the principle of fact-checking scientific claims with Open Access<br> literature, we hope to pave the way to improved automatic fact-checking tools, which<br> will allow an increased understanding of research results by the broad public and to<br> show a strong impact of open science in society.