Projects in SeDiTrah

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© Marcus Winkler on Unsplash

Health-related crises, such as Covid-19, spread fear and uncertainty and give rise to fake news and conspiracy theories, which are further amplified by social media. New policies, scientific discoveries, and ever-evolving misinformation pose severe problems for manual fact-checking, which cannot perform time-aware validation for such complex claims.

In this project, we create a high-quality dataset of real-world Covid-19 claims from social media. To identify evidence for verification or debunking, we find fact-checked counterparts for these claims. We identify misleading claims and annotate them with respect to different pieces of evidence, yielding different veracity labels for a claim and enabling evidence-based evaluation. This allows us to use cutting-edge natural language processing and create transparent models that predict claim veracity and provide rationale for the verdict.

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Peer reviewing is the main quality assurance mechanism in academia and biomedicine, relying on the verification of articles by groups of experts. Recently, the exploding numbers of research articles on covid-19 revealed that traditional peer reviewing is too slow. Consequently, the release of non-refereed articles on pre-print servers like bioRxiv has become increasingly popular, resulting in a vast number of articles of uncertain quality.

Junior researchers offer great, to-date yet unused capacities to scale-up reviewing for assessing pre-print articles. To make this capacity accessible, the goal of this project is to develop an annotation-based peer reviewing assistance tool, compensating for their lack of experience and accelerating the assessment. We integrate cutting-edge natural language processing methods to guide users through the peer reviewing process and facilitate the task by allowing them to naturally make annotations directly in the paper.