Twitter as a social media platform offers up distinct ways for its users to interact with each other. While the list of these may seem limited at first, it is really quite long, as Twitter keeps adding new features to its product (see https://help.twitter.com/en/using-twitter). The most notable features of Twitter, which can also be used as metrics for (data-driven) analysis are:

• Tweets: Tweets are mostly textbased posts, limited to 280 characters. They can also include images, videos, links or polls. A continous series of connected Tweets by a single user is calleda Thread.
• Hashtags: Hashtags can be defined in the text of a tweet by prefixing a single word with the '#' character. Hashtags allow for the grouping of tweets by relating them to a certain topic. Topics with a high current relevance show up in the Twitter Trends.
• Tweet Interactions: A Tweet can be liked and retweeted. There are two kinds of retweets, simple Retweets or Quote Tweets, with the latter on enabling users to add their own comments to a given Tweet. Users can also reply to a given Tweet, with the reply being a Tweet on its own. Tweets can be promoted, which is a paid service to increase the visibility of a Tweet. Tweets can be added to Lists, which are a curated collection of Tweets.
• User Interactions: Users on Twitter are represented by their profile. Profiles can be verified, which helps to identify the real person behind the online persona, and adds credibility and accountability. All users can follow other profiles, and have followers themselves. Users are also able to mute or even block content from other profiles. Other users can be mentioned in Tweets by adding their respective username in the Tweet, prefixed by the '@' character.

While there are numerous other features (Moments, Fleets, …), most of them are not really usable in the context of a data-science project, and therefore have no need to be reagarded here.

A social network can be defined as a series of social units such as people, groups, and organizations with relationships or interactions between them. The underlying structure of such networks is the subject of the study of social network analysis. The main objective of this technique is to examine both the content and relationship patterns in social networks in order to understand the relationships and their effects.

• Tabassum, S., Pereira, F. S. F., Fernandes, S., & Gama, J. (2018). Social network analysis: An overview. WIREs Data Mining and Knowledge Discovery, 8(5). https://doi.org/10.1002/widm.1256

Sentiment analysis, also known as opinion mining, is a sub-area of text mining and describes the automatic evaluation of texts through machine learning and natural language processing. The aim is to capture the author's opinions and emotions. This method can be used in almost all business and social areas, as opinions are essential to human activities and influence behavior.

• Zhang L., Liu B. (2017) Sentiment Analysis and Opinion Mining. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_907

Having an enormous amount of information concerning a special topic or problem is called an infodemic. This information spread rapidly and often include a lot of misinformation. Misinformation is consciously or unconsciously false or inaccurate information to a topic. Often they come along with misquoted sources and fake news. Fake News are manipulatively distributed and simulated news especially spread virally in social networks and media. Disinformation is the dissemination of false information with the aim of influencing public opinion, groups or individuals in the interests of political or economic interests. Disinformation has a great reach in social networks like Twitter. Especially in the last years, mainly because of Covid-19 and trump, many researchers developed methods to identify misinformation on Twitter and did fact-checks on tweets regarding COVID-19.

• Krittanawong, C., Narasimhan, B., Virk, H. U. H., Narasimhan, H., Hahn, J., Wang, Z., & Tang, W. W. (2020). Misinformation dissemination in Twitter in the COVID-19 era. The American Journal of Medicine.