Machine Learning-Based Identifications of COVID-19 Fake News Using Biomedical Information Extraction

The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic. © 2023 by the authors.

COVID-19 clinical medical relationship extraction based on MPNet

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task. © 2023 The Authors. IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets

Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger to people's health, particularly when false information is shared, for instance recommendations on how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for the medical domain are crucial. While existing fact-checking resources cover COVID-19-related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19-related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge indirectly available in pretrained language models. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

BioASQ at CLEF2022: The Tenth Edition of the Large-scale Biomedical Semantic Indexing and Question Answering Challenge

The tenth version of the BioASQ Challenge will be held as an evaluation Lab within CLEF2022. The motivation driving BioASQ is the continuous advancement of approaches and tools to meet the need for efficient and precise access to the ever-increasing biomedical knowledge. In this direction, a series of annual challenges are organized, in the fields of large-scale biomedical semantic indexing and question answering, formulating specific shared-tasks in alignment with the real needs of the biomedical experts. These shared-tasks and their accompanying benchmark datasets provide an unique common testbed for investigating and comparing new approaches developed by distinct teams around the world for identifying and accessing biomedical information. In particular, the BioASQ Challenge consists of shared-tasks in two complementary directions: (a) the automated indexing of large volumes of unlabelled biomedical documents, primarily scientific publications, with biomedical concepts, (b) the automated retrieval of relevant material for biomedical questions and the generation of comprehensible answers. In the first direction on semantic indexing, two shared-tasks are organized for English and Spanish content respectively, the latter considering human-interpretable evidence extraction (NER and concept linking) as well. In the second direction, two shared-tasks are organized as well, one for biomedical question answering and one particularly focusing on the developing issue of COVID-19. As BioASQ rewards the approaches that manage to outperform the state of the art in these shared-tasks, the research frontier is pushed towards ensuring that the valuable biomedical knowledge will be identifiable and accessible by the biomedical experts.