# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 5 Propagation-Based Methods
### Propagation Network Features  
Previous research looks into several features of the networks involved in the propagation of news items. Such features have been used to make important observations regarding the spread of fake news [151; 152]. They have also been used as features for fake news detection [30; 153]. In Table 5, we report several features of propagation networks studied in the context of fake news [30; 154; 155; 156; 151; 152]. Note that features can be computed not only at the whole graph but also at specific subgraphs. The most common _structural features_ are size, depth, and breadth. Size counts the number of nodes, either the nodes representing distinct or the nodes representing non-distinct entities. In the case of tree-structured networks, depth is the maximum distance from the root to any of the leaves, while depth counts the number of nodes at a level of the tree. There are also features of individual nodes, such as the centrality of nodes, as measured by the degree centrality, closeness centrality, and betweenness centrality. Distance-based features consider the shortest path distances between pairs of nodes. One such feature is structural virality, defined as the average Wiener index, i.e., the average shortest path distance between all pairs of nodes [157]. Structural virality tends to be maximized for large networks that have become that way through many small branching events over many generations. Other structural features are about the local structure of the graphs, captured, for example, using modularity. _Temporal features_ have also been considered. In this case, networks are annotated with time information; for example, in a cascade network, each node is annotated with the time when the corresponding item was posted. Common temporal features are lifespan, speed, and heat. Lifespan captures the duration of spreading a news item and can be measured as the difference between the time associated with the first and the last node. Speed refers to how fast information is spread and may be measured as the time needed to reach a specific number of users, a specific level of the tree, or the value of some other structural feature. Heat refers to the number of nodes, e.g., comments that appear within a specific time interval. Several other types of features have also been studied besides structural and temporal ones. A family of such features looks at the users involved in the propagation and computes various statistics about the distribution of their features in the network, such as the percentage of bots. A fairly recent work studies the habitual nature of misinformation propagation. Specifically, Facebook users were found to automatically react to familiar platform cues, sharing misinformation out of habit [156]. For example, headlines were presented in the standard manner (i.e., Facebook format of a photograph, source, and headline) with the sharing response arrow underneath, and users clicked the shared button habitually. Analogously, the distribution of content features, such as linguistic aspects and sentiment, have also been exploited [154]. Finally, there are features about the relationships between the different networks involved. For example, one such feature is whether the likelihood of retweeting is larger between friends. There is also work that aims to understand how misinformation propagates in social networks by identifying influential nodes (e.g., see [158]). Various studies highlight the difference in structural, temporal, and other features between graphs related to fake and true news. The authors of [151] used the retweetnetwork of all fact-checked cascades on Twitter from 2006 to 2017. Their study showed that fake news was propagated six times faster than real news. Specifically, it was shown that it took the truth approximately six times as long as falsehood to reach 1500 people and 20 times as long as falsehood to reach a depth of ten. Falsehood was also diffused significantly more broadly and was retweeted by more unique users than the truth at every depth.