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Found 8 papers, 0 papers with code
Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception
As the argumentation stems from genuine belief, it may be unlikely to exhibit the linguistic properties associated with deception or lying.
Understanding Fine-grained Distortions in Reports of Scientific Findings
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions.
What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification
In a study with trained annotation experts we prompt them to find evidence for biomedical claims, and observe how they refine search queries for their evidence search.
An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking
This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims.
Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets
To make user-generated content checkable by existing models, we propose to reformulate the social-media input in such a way that the resulting claim mimics the claim characteristics in established datasets.
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
The corpus consists of 300 tweets, each annotated with medical named entities and relations.
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Named entity recognition and relation extraction are methods to structure information that is available in unstructured text.
Claim Detection in Biomedical Twitter Posts
We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase).
