# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 5 Propagation-Based Methods
### Propagation Network Features
False political news diffused deeper more quickly and reached more people faster. In addition, the inclusion of bots in social media accelerated the spread of both true and false news. However, it affected their spread roughly equally. In the medical domain, work in [152] constructed cascade networks of retweets during the Zika epidemic in 2016. They report statistically significant differences in nine network metrics between real and fake groups of cascade networks. Fake news cascade networks were in general more sophisticated than those of the real news. The metrics they consider are the number of nodes, number of distinct nodes, diameter, Wiener index, structural virality, out-degree distribution, largest betweenness centrality, modularity, and relation between follower networks and retweets, i.e., the difference in the probability of retweets between followers and non-followers. In a recent study, the authors of [30] examine and analyze several distinct groups of features (introduced in [154]) and various machine learning techniques to assess misinformation in online medicine-related content. Amongst others, they are using propagation network features. They divided these features into four categories: structural features, temporal features, linguistic features, and engagement features. Structural features capture the network structure of users and can be depth, breadth, or out-degree. Temporal features include the duration of dissemination, the average speed of dissemination, and the average speed of response. Linguistic features are variables that capture the linguistic aspects of messages that interact with information dissemination. Finally, engagement features are variables that assess the level of appreciation. After the feature construction, they use binary classification to distinguish health information from misinformation. In their results, they report that the random forest classifier had the best performance among the rest. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Structural Features** \\ \hline Size & Number of nodes or unique nodes \\ Depth & Number of edges from the root to a leaf node \\ Breadth & Number of nodes at some tree level \\ Node centrality & Node degree, betweenness \\ Virality & Average distance between all pairs of nodes (average Wiener index) \\ Modularity & Clustering \\ \hline \hline  & **Temporal Features** \\ \hline Duration & The longest interval \\ Heat & Number of users posting at a specific time interval \\ Speed & Time to reach a specific depth, number of nodes, etc. \\ \hline  & **Other Features** \\ \hline User feature distribution & Distribution of features of users involved in the propagation \\ User habitual reactions & Consistent platform interactions for all news trigger habitual \\  & user reactions \\ Item feature distribution & Distribution of features of items (content) involved in the propagation \\ More than one network & Correlations/relationships between different networks \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Propagation network features. Some propagation-based methods use trees or tree-like structures to capture the propagation of an article on social media. For instance, in [153], the authors propose a graph-kernel-based hybrid SVM classifier that captures the high-order propagation patterns. Specifically, they model the message pattern propagation as a tree that reflects the relation between reposts and their authors. Moreover, they propose a random walk graph kernel to model the similarity of propagation trees. They combined the graph kernel with a radial basis function kernel to build a hybrid SVM classifier. The structural features used for classification were features such as the total number of nodes and the maximum or average degree of the graph. They achieved significantly better classification accuracy than other models for the early detection of false rumors.