Publicly accessible social media posts on platforms such as Twitter provide the raw data for analysis. Many posts contain text and even links. NewsHawk finds and analyzes relevant posts based on text and text-like content plus available metadata.
The search for relevant posts starts with a collection of keywords (or strings). The user compiles them into a list, prioritizes them, and may also link them together. Once found, the posts are stored in a database but are also displayed to users in real time. They are presented in continuously updated widgets, which are prioritized in different ways and can be configured by the user based on a custom tasking.
The database contents are run through a natural language processing (NLP) AI algorithm. This makes it possible to present the data in clusters and categories, based on user preferences. The algorithms can also reconstruct networks that show the people, organizations, places, and so forth, that are mentioned together in a post, or which post authors quote or distribute content from which content creators. It is precisely these interactive networks that allow analysts to gain deeper insights into which post authors could be bots, for instance.