SEMI-SUPERVISED TEXTUAL ANALYSIS AND HISTORICAL RESEARCH HELPING EACH OTHER: SOME THOUGHTS AND OBSERVATIONS

Future historians will describe the rise of the World Wide Web as the turning point of their academic profession. As a matter of fact, thanks to an unprecedented amount of digitization projects and to the preservation of born-digital sources, for the first time they have at their disposal a gigantic collection of traces of our past. However, to understand trends and obtain useful insights from these very large amounts of data, historians will need more and more fine-grained techniques. This will be especially true if their objective will turn to hypothesis-testing studies, in order to build arguments by employing their deep in-domain expertise. For this reason, we focus our paper on a set of computational techniques, namely semi-supervised computational methods, which could potentially provide us with a methodological turning point for this change. As a matter of fact these approaches, due to their potential of affirming themselves as both knowledge and data driven at the same time, could become a solid alternative to some of the today most employed unsupervised techniques. However, historians who intend to employ them as evidences for supporting a claim, have to use computational methods not anymore as black boxes but as a series of well known methodological approaches. For this reason, we believe that if developing computational skills will be important for them, a solid background knowledge on the most important data analysis and results evaluation procedures will become far more capital.