# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 6 Source-Based Methods
### Trolls  
A _troll_ is an individual with antisocial behavior who intentionally creates posts and comments in social media to provoke emotional reactions from others. Such individuals often use aggressive or offensive language in order to slow down the natural progression of the conversation. The purpose of a troll is to offend people, dominate discussions, and manipulate the opinions of people [174].  
The prevalence of trolls has grown in recent years, causing concern for both individuals and society. Identifying trolls is a challenging task, as trolls go to great lengths to conceal their true identity and their actions. Many online communities such as Reddit have developed strategies to identify and remove harmful content like moderation teams that monitor and delete inappropriate messages manually [175; 174]. US Congress also published a catalog of 2752 Twitter accounts which were allegedly linked with the Internet Research Agency, a Russian-based organization referred as a \"troll farm\" [176; 177], in an effort to deter what was perceived as a systematic campaign for the mass manipulation of public opinion by Russia.  
There has been considerable research effort on automatic troll detection approaches [175; 176; 177; 178; 179; 180]. Troll detection involves several methods such as analyzing the language used in posts or comments using NLP techniques to determine the sentiment and emotional tone of messages. User behavior analysis is another method that can be used as trolls often exhibit certain patterns of behavior, such as continuously posting negative comments or targeting specific users. Network analysis is also used as trolls often operate in groups or networks, and by analyzing social network connections, it is possible to identify groups of users engaging in trolling behavior [174].  
For instance, in [178], the authors train a model on 335 Russian-sponsored troll accounts identified by Reddit. They demonstrated that troll accounts exhibit specific interaction patterns, such as a higher frequency of replies among each other, and submissions with the same title. They considered accounts from the top subreddits with trolls (r/News, r/Politics, and r/Bitcoin). They concluded that the most important features include the number of submissions, account age, fraction of submissions with the same title as known troll submissions, fraction of comments that reply to a troll account, etc.  
In [175], the TrollPacific tool is proposed for detecting trolls. This model was trained on Twitter data using six groups of features: sentiment analysis, time and frequency of actions, text content and style, user behaviors, interactions with the community, and advertisement of external content. Community and advertisement groups of features were found to outperform the rest. Additionally, in [179], the authors focused on troll detection in comments via sentiment analysis and user activity data on the Sina Weibo Chinese platform.  
Trolls can also play a significant role in the spread of health misinformation on social media and lead to the confusion of the public. For example, during the COVID-19 pandemic, trolls were very active in spreading health misinformation on a wide range of topics, such as treatments, vaccines, and the origins of the virus [181]. Due to the serious consequences of this kind of misinformation, several troll detection tools have focused on the health domain. For example, in [181], the authors collect a variety of Twitter content featuresto detect whether a tweet was meant to troll. As features, they use trolling hashtags and specific strings that were commonly used to advance trolling topics, sentiment analysis features, and user behavioral features.