Papers by Amelie Wührl
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing fact-checking resources cover COVID-19 related information in news, but there is no dataset providing fact- checked COVId-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence. |
| Approach: | They propose a fact-checked corpus of tweets with annotations for biomedical entities, relations and relevant evidence for COVID-19 related tweets. |
| Outcome: | The proposed dataset provides fact-checked COVID-19 related tweets with detailed annotations for biomedical entities, relations and relevant evidence. |
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR) (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing medical social media corpora focus on a small set of entities and relations . existing text mining and information extraction methods focus on scientific text generated by researchers but their access to individual patient experiences or patient-doctor interactions is limited. |
| Approach: | The dataset consists of 2,100 medical tweets with approx. 6,000 entities and 2,200 relations. |
| Outcome: | The proposed dataset consists of 2,100 tweets with approx. 6,000 entities and 2,200 relations. |
Can Factual Statements Be Deceptive? The DeFaBel Corpus of Belief-based Deception (2024.lrec-main)
Copied to clipboard
| Challenge: | if a person firmly believes in a non-factual statement, there is no inherent intention to deceive. |
| Approach: | They propose to use the DeFaBel corpus to study the relationship between deception and factuality based on belief to generate arguments supporting statements . |
| Outcome: | The DeFaBel corpus contains 1031 texts in german, out of which 643 are deceptive and 388 are non-deceptive. |