<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-family:Segoe UI">To annotate data, which is then being used as training data for the genre classifier, we have added a sample selection of documents. Such a step allows us to annotate texts, which have a high and a low likelihood of representing a specific topic.</hi><ref xmlns="http://www.tei-c.org/ns/1.0" target="ftn7" n="7" /> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-family:Segoe UI" xml:space="preserve"> As figure 2 illustrates, computationally classifying genres does not mean that a text will be 100 % categorized as belonging to one type, but in varying degrees to several types. The genre which achieves the highest score will be decisive for the dataset published in OMEKA S. With this approach, we have identified 10 different newspaper genres. By linking the annotation interface with the OMEKA site, users will be able to gain insight into genre classification process and examine similarities and anomalies between texts and genres using a mixed-methods approach (Sá Pereira 2019). Additionally, scholars can always get redirected to </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic" style="font-family:Segoe UI">Chronicling America</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-family:Segoe UI" xml:space="preserve"> and to examine, for instance, where the text was embedded in the newspaper page. </hi>