# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 6 Source-Based Methods
### Publisher Credibility  
Assessing the credibility of publishers is closely related to the problem of assessing the credibility of web domains. This is a problem that has been considered in the literature for ranking web pages [164; 165] and fighting spam (e.g., see [166; 167]). We aim to assess the credibility with respect to the truthfulness of the information. Assessing the credibility of web domains relies on identifying characteristics and patterns that are associated with fake news. A variety of approaches has been considered, such as content analysis, link analysis, natural language processing, and machine learning algorithms. We distinguish two main categories of methods: methods that use content-based features and methods that use graph-based features. Content-based features extract specific information from the text of websites to distinguish between valid and non-valid websites. For example, in [139], to assess the credibility of a source, the authors extract features from a sample of the website articles, the corresponding Twitter account, the Wikipedia page, and the web traffic information to the domain. The study found that Twitter accounts had the most impactful features. Graph-based features are used to analyze the structure of the network of web pages and the links between them. This can involve identifying patterns of link exchanges and websites that are highly connected to other websites that include fake news. Consequently, \begin{table}
\begin{tabular}{l} \hline \hline
**Propagation-Based Approaches** \\ \hline Explicit features + classification [30; 153] \\ Embeddings + classification [159; 160; 161; 162] \\ Embeddings + node classification (labeling) [162] \\ Link prediction [115] \\ Graph optimization [163] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Propagation-based approaches. most graph-based fake website detection algorithms follow the Pagerank intuition by iteratively propagating scores through the links in the web graph. Using the graph of web pages and links, it is also possible to detect communities of websites that are interconnected and may be involved in spreading fake news. The work in [168] focuses on detecting fake news on web pages utilizing both graph and content-based features. They associate each domain with a group of Twitter users who tweet about it and create a graph by linking domains whose user sets have a Jaccard similarity above a specified threshold. Then, a classifier is used with three types of features: markup, readability, and morphological. The top features associated with real news include advanced HTML tags and high readability scores, while the top features for fake news include italic fonts and underscore. Several studies have indicated that there is a tendency for news publishers (and journalists) to form homogeneous networks. These networks can be analyzed to identify patterns and assess the credibility of unknown authors or publishers. If an unknown author or publisher is found to be connected to a network of individuals that have spread misinformation, it may be reasonable to question their credibility [14]. In [169], the authors explore how the credibility of the news publishers relates to the communities they form. They collected publisher domains and articles from both mainstream outlets and alternative sources. They created a content-sharing directed network, where nodes are news sources and edges are articles copied between the sources. Edges are weighted by the number of articles copied and they are directed from the original source to the source that copied. To identify the communities in the network, they used the modularity maximization algorithm [170]. The credibility of the news sources was then evaluated by using several tools, News Guard, Media Fact-Check, Allsides, and BuzzFeed. They combined the outcomes from these tools to generate a score ranging from \(-1\) to \(1\).