The Transformative Promise of Digital Humanities 1 joris van eijnatten, toine pieters and jaap verheul This article discusses the promises and challenges of digital humanities methodologies for historical inquiry. In order to address the great outstanding question whether big data will re-invigorate macro-history, a number of research projects are described that use cultural text mining to explore big data repositories of digitised newspapers. The advantages of quantitative analysis, visualisation and named entity recognition in both exploration and analysis are illustrated in the study of public debates on drugs, drug trafficking, and drug users in the early twentieth century (wahsp), the comparative study of discourses about heredity, genetics, and eugenics in Dutch and German newspapers, 1863-1940 (biland) and the study of trans-Atlantic discourses (Translantis). While many technological and practical obstacles remain, advantages over traditional hermeneutic methodology are found in heuristics, analytics, quantitative trans-disciplinarity, and reproducibility, offering a quantitative and trans-national perspective on the history of mentalities.

This article aims to offer a methodological contribution to digital humanities by exploring the value of a mixed-method approach to uncover and understand historical patterns in large quantities of textual data. It refines the distant reading technique of topic modelling (TM) by using the discourse-historical approach (DHA——Wodak, 2001) in order to analyse the mechanisms underlying discursive practices in historical newspapers. Specifically, we investigate public discourses produced by Italian minorities and test the methodology on a corpus of digitized Italian ethnic newspapers published in the USA between 1898 and 1920 (ChroniclItaly—Viola, 2018). This combined methodology, which we suggest to label ‘discourse-driven topic modelling’ (DDTM), enabled us to triangulate linguistic, social, and historical data and to examine how the changing experience of migration, identity construction, and assimilation was reflected over time in the accounts of the minorities themselves. The results proved DDTM to be effective in obtaining a categorization of the topics discussed in the immigrant press. The changing distribution of topics over time revealed how the Italian immigrant community negotiated their sense of connectedness with both the host country and the homeland. At the same time, without jeopardizing the analytical depth of the findings, the method proved its value of minimizing the risk of biases when identifying the topics which stemmed from the results rather than from preconceived assumptions.

This study proposes an experimental method to trace the historical evolution of media discourse as a means to investigate the construction of collective meaning. Based on distributional semantics theory (Harris, 1954; Firth, 1957) and critical discourse theory (Wodak and Fairclough, 1997), it explores the value of merging two techniques widely employed to investigate language and meaning in two separate fields: neural word embeddings (computational linguistics) and the discourse-historical approach (DHA; Reisigl and Wodak, 2001) (applied linguistics). As a use case, we investigate the historical changes in the semantic space of public discourse of migration in the United Kingdom, and we use the Times Digital Archive (TDA) from 1900 to 2000 as dataset. For the computational part, we use the publicly available TDA word2vec models 1 (Kenter et al., 2015; Martinez-Ortiz et al., 2016); these models have been trained according to sliding time windows with the specific intention to map conceptual change. We then use DHA to triangulate the results generated by the word vector models with social and historical data to identify plausible explanations for the changes in the public debate. By bringing the focus of the analysis to the level of discourse, with this method, we aim to go beyond mapping different senses expressed by single words and to add the currently missing sociohistorical and sociolinguistic depth to the computational results. The study rests on the foundation that social changes will be reflected in changes in public discourse (Couldry, 2008). Although correlation does not prove direct causation, we argue that historical events, language, and meaning should be considered as a mutually reinforcing cycle in which the language used to describe events shapes explicit meanings, which in turn trigger other events, which again will be reflected in the public discourse.

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