# Health Misinformation in Social Networks: A Survey of Information Technology Approaches
## 6 Source-Based Methods
### Social Bots  
A _bot_ is a software program designed to automate tasks or simulate human behavior on the internet. There is a wide range of different types of bots, such as spiders or crawlers, shopbots, knowbots, chatbots, and social bots. A social bot is a type of bot that imitates human behavior in social media platforms. Social bots can be utilized for both positive and negative purposes [182]. Some of the positive purposes are disseminating accurate and helpful information to a wide audience, promoting products or services in a cost-effective manner, and providing customer support [183]. Also, social bots have helped in emergency management like in publishing earthquake warnings in Japan [184]. However, they have also been used for negative purposes such as spreading misinformation and propaganda to manipulate public opinion, scamming people, and creating fake accounts to manipulate or increase follower counts. Social bots, in some cases, have some similarities with trolls. Both bots and trolls can be used to spread fake news and propaganda and influence public opinion. They are often designed to spread malicious software, create confusion and distraction, and manipulate social media discourse with rumors [185]. They can infiltrate into a group of people and change their view of reality [186]. Some notable examples are the role of social bots on the Brexit debate where they influenced users opinion about the election [187] or the effect of social bots on Twitter during the 2016 U.S. election. Some studies claim that 64% to 79% of [PERSON]'s followers were fake. Such accounts made opinions appear more widespread [188]. The effect of bots on health-related news can be really harmful. Bots can spread fake or misleading information about health topics and cures, which can lead to confusion and harm public health. During the COVID-19 pandemic, the majority of social bots were tweeting about COVID-19 [189]. However, there is limited evidence regarding their stance on these topics. Some bots were spreading accurate up-to-date information about the pandemic and others were spreading misinformation, conspiracy theories, fake treatments, and rumors about the vaccine. It is shown that the activity level of social bots was consistent with that of human users [190]. Nevertheless, in [191], by utilizing a combination of machine learning and network science methods on a collection of tweets about the pandemic in the Philippines and the United States, the authors indicate a significant association between bot activity and increased levels of hate speech in both countries, particularly within densely populated and socially isolated communities. Additionally, they highlight the significance of adopting a global perspective in computational social science, especially in the midst of a worldwide pandemic and infodemic, which have universal yet unequal impacts on societies. To detect bots, various techniques, similar to those used in troll detection, are employed [189]. Profile metadata include account age and username, along with user activity, temporal patterns, and post content. Bots tend to retweet more frequently and have shorter time intervals between tweets. Additionally, network patterns, such as friend/follower connections, hashtag use, retweets, and mentions are used to identify social bots that exploit network homophily. Bot detection is getting harder as bots evolve and make it more difficult to distinguish them from human behavior [186]. In [192], the authors collected tweets and analyzed user activity by measuring the number of tweets, retweets, and replies. They also examined the types of software used to generate tweets (some users use Twitter clients, some others use cross-posting through other platforms, while commercial actors use marketing automation software).