# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 5 Propagation-Based Methods
### Beyond Propagation Features  
Recent research leverages complex graphs beyond the propagation networks to leverage rich contextual information about the news and users involved in spreading misinformation. Entities involved in the news propagation include the original news item, related posts, publishers, and users. Edges in the graphs correspond to the various relations between these entities. Initial feature representations of the nodes of the graphs are created using content and other features. These representations are then enhanced using the relations in the graphs. Approaches differ in the types of entities, relations, and graphs as well as in the methods used to create latent representations. Below we present some representative approaches that use such complex networks for fake news detection [159; 160; 161; 162]. Hetero-SCAN [159] builds a heterogeneous graph capturing various types of nodes and interactions. Specifically, the graph consists of three types of nodes: publishers who create the news, users who tweet the news, and news. There are four types of edges: citation edges between publishers, publish edges between publishers and news, tweet edges between users and news, and friendship edges between users. The graph is used to learn feature representations for the news nodes, which are used as input to a fake news classifier. Initial feature representations are constructed for the three types of nodes using appropriate text- and graph-embedding methods. Then, the feature representations for the news nodes are enriched using instances of two types of meta-paths: news \(\rightarrow\) publishers \(\rightarrow\) news and news \(\rightarrow\) users \(\rightarrow\) news. KG-based embeddings are used, in particular TransE, refined for this task. The premise behind using these specific meta-paths is that the validity of a specific news item is related to the authenticity of both its publishers and its users as well as the news that the publishers and users, respectively, publish and tweet. The SOMPS-Net framework builds an end-to-end binary fake news classifier [160]. The entities are news articles, tweets, and retweets of the articles, users, and publishers. SOMPS-Net consists of two components, the social interaction graph component (SIG) and the publisher and news statistics component (PNS). The SIG component has several subcomponents. Initial textual embeddings of each article and its related tweets and retweets are generated using GloVe. Feature vectors are constructed for the users based on information from their profile and history, such as the number of their tweets and friends. For each article, two social connectivity graphs are built, one with the users that tweeted about the article and one with the users that retweeted about it. There is an edge from a user \(u\) to a user \(v\) if \(u\) follows \(v\) weighted by the number of common followers and followees of \(u\) and \(v\). A graph convolution layer is applied on the two graphs to obtain graph embeddings. A cross-attention component is then applied to capture the correlation between the news embeddings and the graph embeddings. The PNS component includes statistical features about the article, such as the total number of tweets, and some features of its publisher. The PNS feature vector is fused with the output of the SIG component, and the result is used to predict the validity of a news item. Fake news detection is formulated as a binary graph classification problem in the Post-User Interaction Network (PSIN) model [161]. The entities are news items, posts, and users. An interesting aspect of the approach is that they adopt an adversarial topic discriminator to improve the generalizability of the model to emerging topics. A heterogeneous directed graph is built for each news item. The graph includes two types of nodes: the users and the posts involved in the dissemination of the item. There are three types of edges: propagation edges between posts, follower edges between users, and post edges from users to posts.