# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 5 Propagation-Based Methods  
Propagation-based methods leverage the complex networks involved in and influencing the dissemination of news within social media platforms. These approaches analyze how misinformation spreads by examining user behavior, the structural and temporal dynamics of interactions, and the characteristics of both the users and their social connections. By studying engagement patterns, such as how quickly and widely false information circulates, these methods aim to identify signals that distinguish fake news from legitimate content.  
An important network is the _cascade network_ that captures the actual propagation of a news item in a social network. The cascade network is usually a tree whose nodes represent the users who are involved in spreading each news item. The root represents the publisher of the item, i.e., the user who first posted the item in the social network. There is a link from a node \(u\) to a node \(v\) if the user represented by \(u\) has spread the item posted by the user represented by \(v\), for example, by retweeting, reposting, or liking the item. Spreading news in such networks indicates endorsement of the original item. Note that for a specific news item, there may be more than one publisher and thus multiple simultaneous cascades.  
Another network directly capturing the propagation of an item is the _discussion_ network. This is usually a tree whose nodes represent items related to the news item. The root represents the original news item, and there is a link for a node \(u\) to node \(v\) if the item represented by \(u\) is a reply or a comment to the item represented by node \(v\). A common variation of the discussion network is one whose nodes represent the users that posted the items instead of the item itself. Spreading items in such networks indicates engagement with the original item but not necessarily an endorsement of it, since a reply or a comment may express a negative reaction.  
Several other networks have also been studied; for example, networks that capture a friendship or follow relationships between the users spreading the news, as well as specialized healthcare social networks [150]. We present some of them later in this section.  
Propagation networks find many applications in the context of medical fake news. First, they have been exploited toward enhancing our understanding of the mechanisms driving the spread of fake news in a social network. Then, propagation networks are used to improve fake news detection. Furthermore, they help in building richer user and item profiles. For example, a friendship network can be used to build an enhanced profile for a user \(u\) that also incorporates information about the friends of \(u\), or a discussion network can be used to augment the content of an item \(i\) with the content of replies to \(i\). This can be achieved either through explicit aggregation or implicitly by using the propagation network to build, for example, a GNN architecture.  
In the following, we first present propagation-based approaches that involve a feature-extraction process. Then, we present approaches that use latent features of the networks.