# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 3 Fact-Checking
### Manual Fact-Checking
#### 3.1.1 Expert-Based Fact-Checking  
In expert-based fact-checking, the news is checked by experts of a domain, like the previously mentioned COVID-19 fact-checkers network. Usually, this team consists of journalists and domain experts. This method offers accurate fact-checking but is expensive and time-consuming. Moreover, it is difficult for a group of people to cover all the current affairs articles and keep up with rapidly changing domains like the COVID-19 pandemic everyday.  
The most famous fact-checking site that makes use of an expert-based fact-checking method and was highly active during the pandemic is Politifact [36], which contains a column about health news and a column about coronavirus. In Politifact, a team of experts studies daily transcripts, news stories, press releases, and campaign brochures to find the most significant claims. Politifact uses the Truth-O-Meter ratings that classify the claims into the following categories: true (accurate), mostly true (needs clarification), half-true (leaves out key details), mostly false (ignores critical facts), false (not accurate), and pants on fire (ridiculous claim).  
Another platform is the Media Bias/Fact-Check [37] where the team uses a 0-10 scale to rate sites for biased wording, headlines, actuality, sourcing, story choices, and political affiliation. The team sorted the various sources into the following bias categories: left, left center, right center, right, least biased, conspiracy pseudoscience, questionable Sources, pro-science, and satire.  
Based on the work of expert fact-checkers, a number of datasets have been constructed for facilitating research for claim detection and verification. A well-known generic fake news dataset is FEVER [38]. It consists of almost 200K claims manually verified against Wikipedia pages and classified as supported, refuted, or notenoughinfo. Datasets that focus on assessing the veracity of scientific claims include the SciFACT ([[https://github.com/allenai/scifact](https://github.com/allenai/scifact)]([https://github.com/allenai/scifact](https://github.com/allenai/scifact)), accessed on 10 December 2024) [39] and the larger SciFACT-Open ([[https://github.com/dwadden/scifact-open](https://github.com/dwadden/scifact-open)]([https://github.com/dwadden/scifact-open](https://github.com/dwadden/scifact-open)), accessed on 10 December 2024) [40] datasets. These datasets include expert-written claims paired with evidence-containing abstracts annotated with veracity labels and rationales. Datasets that focus on the opposite task of non-claim detection also exist [41]. Finally, there exists a dataset ([[https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP2/VPYSIS](https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP2/VPYSIS)]([https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP2/VPYSIS](https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP2/VPYSIS)), accessed on 3 November 2023) containing COVID-related claims that have been reviewed by fact-checking organizations around the world and retrieved by the Google Fact-Check Tools API ([[https://toolbox.google.com/factcheck/explorer](https://toolbox.google.com/factcheck/explorer)]([https://toolbox.google.com/factcheck/explorer](https://toolbox.google.com/factcheck/explorer)), accessed on 10 December 2024).