# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 5 Propagation-Based Methods
### Beyond Propagation Features
The heterogeneous graph along with the topic of a news item are given as input to a classifier that determines whether the item is fake or not. To process the graph, the graph is decomposed into three parts: the post-propagation tree, user social graph, and post-user interaction graph. Node representations are built using both textual features and meta-features (e.g., number of likes). Each part of the graph is processed individually, using appropriate graph attention (GAT) models. At the end, the concatenated and pooled representations are fed into a veracity classifier and an adversarial topic classifier. A different approach is taken in [162], where a new graph data structure, termed meta-graph, is proposed. The nodes of the meta-graph correspond to retweet cascades. Each cascade disseminates a root tweet along with the web and/or media URL(s) embedded in that tweet. There is an edge between two nodes (i.e., cascades) if there are common users in the two cascades. The node features contain information about the cascade graph structure, obtained by applying a graph-embedding algorithm to each individual cascade, textual information potentially including topic and sentiment, profile user information, and relational user information, extracted from the social network. The edge features encode relationships between cascades, such as structural similarity, number of common users, or content similarity. Two formulations of the problem of fake news detection are proposed. The first formulation is as a graph classification problem where each graph corresponds to a single cascade, i.e., the meta-graph is not used. The second formulation is as a node classification problem at the meta-graph level. The meta-graph node classification approach slightly outperforms the graph classification approach in accuracy. Various GNN-based classifiers are used in both approaches. Complex networks are used to determine whether an item refers to a known misconception in [115]. Specifically, given a set of misinformation targets (MisTs), each addressing a known misconception about the COVID-19 vaccines, the authors address the problem of discovering automatically which tweets contain misinformation and the one or more MisT they refer to. They construct a Misinformation Knowledge Graph (MKG) whose nodes are tweets that contain misinformation. An edge between two tweets indicates that they share the same misconception, and the tweets are labeled with the corresponding MisT. For each MisT, a separate, fully connected graph (FCG) is generated spanning all tweets that refer to this MisT. Then, the problem of discovering whether an unconnected tweet contains misinformation is cast as a link prediction problem: predict whether there is a link from the tweet to any FCG. Representations of the tweets are learned using several KG embedding models. To capture linguistic content, embeddings of MisTs and tweets are also created using COVID-Twitter-BERT-v2 and projected in the embedding space of the MKG. The link ranking functions from each KG embedding model are used for predicting a link between any tweet that may contain misinformation and tweets that share a known MisT. Finally, an additional type of information that can be exploited is the fact that an item may take a different stance on a topic. To exploit this information, some approaches introduce stance networks where different types of edges are used to exploit agreement or disagreement between nodes (e.g., news items or users). For example, the approach proposed in [163] exploits a signed network of tweets to predict the credibility of a tweet. Credibility is a numeric value in \([-1,1]\) with positive values corresponding to valid information and negative values to fake information. There are two types of edges: positive edges between tweets taking the same viewpoint and negative edges between tweets taking different viewpoints. Initial credibility values are assigned to some tweets. Then, credibility propagation is formulated as a graph optimization problem.