# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 4 Content-Based Methods
### Entity-Based Features  
News usually contains named entities, which can be important for understanding the news semantics and for detecting fake news. Such entities include categories like person, PersonType, location, organization, event, product, dates, and others. The process of identifying the named entities is called Named Entities Recognition (NER) (a list of NER tools is provided at [[https://www.clarin.eu/resource-families/tools-named-entity-recognition](https://www.clarin.eu/resource-families/tools-named-entity-recognition)]([https://www.clarin.eu/resource-families/tools-named-entity-recognition](https://www.clarin.eu/resource-families/tools-named-entity-recognition)), accessed on 10 December 2024). Biomedical NER has also been studied for recognizing diseases, drugs, surgery reports, anatomical parts, and examination documents [143; 144; 145]. In addition, the previously presented language models can be fine-tuned for this specific task [99; 146]. NER approaches are usually deployed in fake news classification as a feature that enriches the textual representation, sentiment, and linguistic features [87; 130]. For example, in [130] the authors use a combination of POS tagging and NER sequences to identify valid and fake news. The work described in [30] uses linguistic-medical entities and specifically the fraction of medical terms in a document. Those are found using the MedMentions ([[https://github.com/charzuckerberg/MedMentions](https://github.com/charzuckerberg/MedMentions)]([https://github.com/charzuckerberg/MedMentions](https://github.com/charzuckerberg/MedMentions)), accessed on 10 December 2024) NER model, which is specially trained on medical information, and consists of 4392 biomedical papers annotated with mentions of UMLS entities. They also exploit commercial terms since they are used for profit. As a result, the higher the number of commercial terms, the less credible the information [147]. The authors in [88] explore to what extent credible medical-related sources make use of quotations and hyperlinks in a news article by using the Stanford QuoteAnnotator ([[https://github.com/muzny/quoteannotator](https://github.com/muzny/quoteannotator)]([https://github.com/muzny/quoteannotator](https://github.com/muzny/quoteannotator)), accessed on 10 December 2024). What they observed is that unreliable outlets use a smaller number of quotations compared to reliable ones. Regarding the hyperlinks, they concluded that a credible news article contains on average 1.6 more hyperlinks than non-credible articles. They also compare various classifiers using textual representations, the headline \begin{table}
\begin{tabular}{l c c} \hline \hline  & **General** & \\ \hline BERT & English Wikipedia, BooksCorpus & [46] \\ BART & English Wikipedia, BooksCorpus & [51] \\ RoBERTa & English Wikipedia, BooksCorpus, & [94] \\  & CC-News, OpenWebText, & \\  & and CommonCrawl Stories & \\ distilBERT & English Wikipedia, BooksCorpus & [95] \\ Albert & English Wikipedia, BooksCorpus & [96] \\ XLNet & English Wikipedia, BooksCorpus & [97] \\ \hline \hline  & **Fake News** & \\ \hline FakeBERT & 40K fake and valid news & [98] \\ \hline \hline  & **Medical Domain** & \\ \hline cui2 vec & 20M clinical notes and & [71] \\  & 1.7M biomedical articles & \\ BioBert & PubMed abstracts and PMC articles & [99] \\ Med-BERT & Electronic health records & [100] \\ SCIBERT & FakeHealth dataset (news) & [101] \\ COVID-Twitter-BERT & 22.5M COVID-19 tweets & [110] \\ BioGPT & 15M PubMed abstracts & [102] \\ \hline  & **Social Media** & \\ \hline BERTweet & 850M English tweets & [109] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pre-trained models: general, fake-news-, medical-domain-, and social-media-related ones over various datasets. length, the number of direct quotes, and the number of hyperlinks and showcase the importance of direct quotes and hyperlinks for detecting fake news. Hashtags have also been found effective in identifying fake news, especially early in the propagation lifecycle [132].