# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 3 Fact-Checking
### Automatic Fact-Checking
#### 3.2.1 Claim Detection  
Regarding the detection of the claims contained in social media posts, comments, news, and web pages, it is important that the most check-worthy claims are selected. Such claims are those for which people show interest and are trending. The claims are usually collected from social media, where metadata like the number of likes and reposts are used as features to identify top claims [30]. Other sources of claims can be found in Wikipedia (e.g., COVID-19 pandemic misconceptions) or in news websites and organizations.  
In [6], the authors annotate a corpus of 1200 tweets for implicit and explicit biomedical claims. Using this corpus, which is related to COVID-19, measles, cystic fibrosis, and depression, they developed deep learning models that automatically detect tweets containing claims. Their analysis showed that biomedical tweets are densely populated with claims.  
Despite the fact that the detection of claims was challenging, they report that deployed models provided an acceptable performance.  
While most works focus on single claim sentence analysis, the work presented in [44] introduces the NewsClaims dataset, a benchmark for attribute-aware claim detection considering topics related to COVID-19. Specifically, given a news article, the task is to identify the claim sentence to a set of predefined topics that contain factually verifiable topics, the claimer, the claim object, the stance of the claimer, and the exact claim boundaries. For claim sentence detection, they use Claimbuster ([[https://idir.uta.edu/claimbuster/api/](https://idir.uta.edu/claimbuster/api/)]([https://idir.uta.edu/claimbuster/api/](https://idir.uta.edu/claimbuster/api/)), accessed on 10 December 2024) [45] along with pre-trained Natural Language Inference (NLI) models as zero-shot classifiers based on BART, where the claim sentence is the NLP premise and the hypothesis is constructed from each topic. The claim object task is modeled as a zero-shot or few-shot setting by converting it into a prompting task for pre-trained language models like GPT-3 ([[https://lablab.ai/tech/gpt3](https://lablab.ai/tech/gpt3)]([https://lablab.ai/tech/gpt3](https://lablab.ai/tech/gpt3)), accessed on 10 December 2024). Stance detection is performed again through NLI, where the affirm and refute labels construct the hypothesis, taking as stance the corresponding higher entailment score. Claim boundary detection is performed using fine-tuned Bidirectional Encoder Representations from transformers (BERT) [46] models, the Project Debater APIs [47], and the PolNeAR ([[https://github.com/networkdynamics/PolNeAR](https://github.com/networkdynamics/PolNeAR)]([https://github.com/networkdynamics/PolNeAR](https://github.com/networkdynamics/PolNeAR)), accessed on 10 December 2024) popular news attribution corpus of annotated triples comprising the source, cue, and content for statements made in news. Finally, regarding claimer detection, they again leverage PolNeAR for building a claimer extraction baseline by fine-tuning a BERT model, along with a second baseline built upon Semantic Role Labeling (SRL), that outputs the predicate-argument structure of a sentence such as who did what to whom. The results showcase that the above tasks, except the task of stance detection, are difficult for current models, especially the task of claim sentence detection.