  Travelogues represent an important and intensively studied source for
scholars in the humanities, as they provide insights into people, cultures, and
places of the past. However, existing studies rarely utilize more than a dozen
primary sources, since the human capacities of working with a large number of
historical sources are naturally limited. In this paper, we define the notion
of travelogue and report upon an interdisciplinary method that, using machine
learning as well as domain knowledge, can effectively identify German
travelogues in the digitized inventory of the Austrian National Library with F1
scores between 0.94 and 1.00. We applied our method on a corpus of 161,522
German volumes and identified 345 travelogues that could not be identified
using traditional search methods, resulting in the most extensive collection of
early modern German travelogues ever created. To our knowledge, this is the
first time such a method was implemented for the bibliographic indexing of a
text corpus on this scale, improving and extending the traditional methods in
the humanities. Overall, we consider our technique to be an important first
step in a broader effort of developing a novel mixed-method approach for the
large-scale serial analysis of travelogues.
