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

摘要

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 finegrained 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.