Studying how the past is remembered: towards computational history through large scale text mining

«History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societal views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support».


(…) Every year, at the genocide-commemoration ceremonies during mourning week, scores of Rwandans erupt in this way, unstrung by grief, convulsed and thrashing when anyone comes near to soothe or subdue them, including, at the stadium, yellow-vested trauma teams who carry them out, bucking and still screaming. You can expect it, but you can’t protect against it. All around the stadium, all around the city, all around the country hung misty-gray banners displaying the word kwibuka—“remember.” The lacerating voices in the stadium make the banners seem almost cruel. Is it really healing to keep reopening a wound? (…)