# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 6 Source-Based Methods
### Social Media Users  
We now turn our attention to the credibility assessment of users in social media. We divide social media users into two categories: humans (normal, non-automated users) and bots (fully automated accounts). The assessment of social media user credibility is based on a diverse range of source-based features, including _user_, _content_, _network_, _behavioral_, and _temporal_ characteristics. User features include basic information such as age, profile, and interests, while content features pertain to the textual information found in the user profile, posts, and comments. Behavioral features refer to the user behavior on the platform, such as their discussion initiation, interaction engagement, influential scope, and more. These features can provide insights into the user's level of engagement with the platform and their role in the community. Timing features, such as interval entropy, can also be analyzed to determine if the user is sharing content in a manner consistent with typical human behavior. Table 7 presents a summary of the most commonly used features in source-based methods. In [128], the authors propose an approach for the early detection of fake news about COVID-19 from social media, such as tweets in multiple indic languages besides English, using both _content_ and _source_ based features. To determine the credibility of the source, they use _user features_ from Twitter and _network features_. Some of the features as reported in Table 7 are the number of characters and the total number of URLs in the description of the user, whether the user has an official URL or not, the number of followers, the number of days since the user's latest tweet, whether the user account is verified officially, etc. After experiments on various combinations of features and classifiers, they concluded that user features are effective at determining the credibility of the news that the user spreads. Another approach [173] focuses on _behavioral features_ of users in order to detect misinformation spreading on Zibizheng Ba, a Chinese forum about autism on Baidu Rieba. Specifically, they measured discussion initiation (the tendency of a user to initiate a new discussion); interaction engagement (how much a user is interacting with other users); influential scope (the communication ability of a user) that is measured by using the degree centrality in the social network of the user; and relation mediation (the ability to control communication in the community), measured by betweenness centrality and informational independence (the ability of the user to instantly communicate with others without going through many intermediaries) and by measuring closeness centrality. Some other medical fake news detection approaches that make use of source-based methods examine the characteristics of users who are sharing unverified cures for various illnesses. In [131], the authors focused on misinformation spread about cancer treatments by users on Twitter. They separate users into two groups, the _rumor_ group, with users that have posted some of the \"treatments\", and the _control_ group, who have posted generally about cancer but none of these topics. They measure and compare _user features_, _content features_, and _temporal features_. They observed that the most important and discriminative features of content-based features are readability, number of adverbs and numbers, user verification regarding the user features, and interval entropy regarding temporal features. Also, they concluded that most viral rumors were on \"cures\" that involved food or drink. The importance of the knowledge background of a user when dealing with healthcare information dissemination is studied in [172]. The authors analyze Twitter users and their content in the dissemination of medical news around mammography and the ensuing discussions. They separate users in non-healthcare, health organizations, commercial