# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 3 Fact-Checking
### Automatic Fact-Checking
#### 3.2.2 Claim Validation  
For the assessment of the validity of a claim, an essential part is the retrieval of the evidence process. Evidence retrieval is the task of retrieving documents that support the prediction of a claim. During this process, information and proofs must be found around the claim, such as the text, tables, knowledge bases, images, and other metadata for evidence of the truth. A fundamental issue is finding trustworthy sources. For example, many fact-checking approaches make use of encyclopedias. Table 1 reports some trustworthy sites for information and proof of medical claims. After the retrieval of evidence, a fact-verification method has to conclude the validity of a claim. Usually, the verification of claims leverages NLI techniques [48]. As already mentioned, the NLI task aims to classify the relationship between a pair of a premise (evidence) and a hypothesis (claim) as either entailment, contradiction, or neutral. However, in fact-verification systems, the usually multiple evidence pieces are found by the systems themselves. In addition, given a collection of false/true claims, the verification of a new information piece can also be modeled as an NLI task, where the goal is to detect entailment with one of the false/true collected claims [49]. Another approach is described in [50], where the authors present the ClaimGen-BART and Knowledge Base Informed Negations (KBIN) methods for generating claims and claim negations supported by the literature, using the BART pre-trained model [51]. In [39], the authors present the Kernel Graph Attention Network (KGAT), which conducts more fine-grained fact verification with kernel-based attention. Regarding the medical domain, a COVID-19-specific dataset that has been constructed using automatic methods is COVID-Fact [52], which contains 4086 claims concerning the pandemic, evidence for the claims, and contradictory claims refuted by evidence. The approach described in [53] adapts the open-domain fact extraction and verification KGAT approach [54] with in-domain language models based on the SciFACT and COVID-Fact datasets. Specifically, the in-domain language model transfers COVID domain knowledge into pre-trained language models with continuous training. The COVID medical token semantics are learned using mask language model-based training. In a similar manner, the authors of [55] introduce the PubHealth dataset for public health fact-checking, which also includes explanations, and explore veracity prediction and explanation generation tasks using various pre-trained models. Their results show that training models on in-domain data improves the accuracy of veracity prediction and the quality of generated explanations compared to training generic language models without explanation. In [56], the authors introduce the HealthVer dataset for evidence-based fact-checking of health-related claims. The dataset was created using a three-step approach. The first step is to retrieve real-world claims from snippets returned by a search engine for questions about COVID-19. The next step is to retrieve and rank relevant scientific papers as evidence from the COVID-19 Open Research Dataset (CORD-19) [9] using a T5 ([[https://github.com/google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer)]([https://github.com/google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer)), accessed on 10 December 2024) relevance-based model. The last step is to manually annotate the relations between each evidence statement and the associated claims. The conducted experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims. In [57], the authors evaluate baseline models for categorizing Reddit posts as containing claims, personal experiences, and/or questions.