{"id":8556,"date":"2019-11-27T13:59:00","date_gmt":"2019-11-27T12:59:00","guid":{"rendered":"https:\/\/www.uni.lu\/c2dh-en\/?post_type=articles&p=8556"},"modified":"2025-04-15T13:50:28","modified_gmt":"2025-04-15T11:50:28","slug":"lecture-by-melvin-wevers-signals-and-noise-extracting-patterns-of-cultural-expressions-from-digitized-sources","status":"publish","type":"articles","link":"https:\/\/www.uni.lu\/c2dh-en\/articles\/lecture-by-melvin-wevers-signals-and-noise-extracting-patterns-of-cultural-expressions-from-digitized-sources\/","title":{"rendered":"Lecture by Melvin Wevers: “Signals and Noise: Extracting Patterns of Cultural Expressions from Digitized Sources”. Recording and interview."},"content":{"rendered":"\n
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\nLecture by Dr. Melvin Wevers, KNAW Humanities Cluster, Amsterdam, and exclusive interview for the C\u00b2DH\u2019s \u2018New Horizons\u2019 lecture series.<\/h5>\n\n\n\n

Recent years have seen an explosive growth of digitized historical material. Innovations in natural language processing have extended the possibilities for historians to extract information from large corpora of digitized texts. In debates on big data, it is often claimed that by \u2018distant\u2019 reading huge amounts of data, textual patterns will emerge, that will allow us to answer historical questions in a radically new way. However, in order to better understand patterns in historical data we need to rely on modelling techniques that make statistical inferences and estimations. The use of modelling can help historians to answer their questions in reproducible and transparent manners. The current situation is strikingly similar to the situation that economic historians were in during the 1950s and 1960s. In this period, a growing body of mathematical formalizations of economic theories combined with increasing computing power pushed the field of cliometrics: a quantitative approach to economic history. In his lecture, Wevers focused on how we can extract patterns of cultural expression from digitised newspapers or, put differently, how we can separate signal from noise in large collections of text. M. Wevers argues that modelling can help researchers to better understand historical dynamics.<\/p>\n\n\n\n

\nRecording of the conference<\/h3>\n\n\n
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