Conceptual History Using Digital Methods
Harald Kümmerle, History of Science / Digital Humanities
Computer-aided text mining facilitates the systematic examinationof large amounts of texts. This is a very promising method for thehistory of concepts, since it can eliminate the need to limit oneself
to a small number of key texts. If the functionality of the algorithms
and the corresponding limitations are disclosed, a connection tomore classical methods of textual analysis enables a combination ofthe advantages of quantitative and qualitative research.

LDA topic models

Since the 2000s, the method of modeling topics in a text corpus
using so-called Latent Dirichlet Allocation (LDA) has emerged as analternative to older algorithms such as dictionary-basedclassification and cluster recognition. The topic models that emerge
are characterized by the property that they inherently take into
account the ambiguity of words (Blei et al., 2003).

Operationalization
A direct evaluation of the topic models can be understoodas distant reading of the analyzed text corpus in the sense ofFranco Moretti (2013). Usually, this is supplemented with a close
reading of individual texts from the corpus. By systematicallylinking these steps with the Grounded Theory Method (Baumer etal., 2017; Nelson, 2020), the research project connects the analysis
using LDA topic models to the history of concepts. While Reinhart
Koselleck (1972) has developed conceptual history for the study ofsocial history, Hans-Jörg Rheinberger (2008) contributed to its
adoption in the history of science placing an emphasis on hybridity.
The situation for “data” is similar.

Case study: the concept of data
This project also provides the methodology for a study of the digitaltransformation in Japan (e.g. Kümmerle, 2022) based on theconcept of data. The main corpus consists of over 160.000 speech
contributions in the Japanese National Diet from 2011 to 2021 that
contain the word dēta (the common translation of “data” intoJapanese) or other words closely connected to its reception history,e.g. shiryō or yoken. With the right parameters, the topic modelingalgorithm succeeds in identifying the two distinct meanings of dētathat correspond with the two different meanings in dictionaries:(1) facts for devising an argument and (2) encoded material forcomputation. This provides the basis for a systematic analysis.

Topic models can handle hybrid concepts (Blei, 2012)

Layered traditions

Here, both the specifics of text genres and the diversity of themetadata which can guide the creation of categories must be
considered. Working with Japanese-language text corpora isparticularly informative not only because of the logogram characterof the Chinese characters. It is also significant that many concepts
were newly created during the reception of knowledge from Chinaand Europe or were influenced hereby, but then developed asubstantial momentum of their own, often with connotations thatare in part independent from their origins. The concept of
information is instructive: In Japan, the word 情報 (jōhō) wascoined in the 19th century in context of intelligence-gathering by
the military and the state. Taken over into Chinese and read as
qíngbào, this connotation is still dominant in China. In Japan,
however, the word became neutral during the postwar democracy(Nakamoto, 2002). Japan’s attempts to establish a system of“information banks” (jōhō ginkō) for sharing private data(Kümmerle, 2023) has to be understood against this backgroundand creates fruitful tensions with the concept of data.

Literature

Two meanings of dēta are identified and tracked over timeBaumer, E. P. S., Mimno, D., Guha, S., Quan, E., & Gay, G. K. (2017). Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence? Journal of the Association forInformation Science and Technology, 68(6), 1397–1410.
Blei, D. M. (2012). Probabilistic Topic Models. Commun. ACM, 55(4), 77–84.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3, 993–1022.
Koselleck, R. (1972). Begriffsgeschichte und Sozialgeschichte. In P. C. Ludz (Ed.), Soziologie und Sozialgeschichte: Aspekte und Probleme (pp. 116–131). Opladen: Westdt. Verl.
Kümmerle, H. (2022). Japanese data strategies, global surveillance capitalism, and the “LINE problem.” Matter: Journal of New Materialist Research, 3(1), 134–159.
Kümmerle, H. (2023). More Than a Certification Scheme: Information Banks in Japan Under Changing Norms of Data Usage. In A. Khare & W. W. Baber (Eds.), Adopting and Adapting Innovationin Japan’s Digital Transformation. Singapore: Springer Nature.
Moretti, F. (2013). Distant Reading. London: Verso.
Nakamoto H. (2002). Yōgo “jōhō”—Tāminojorīteki kōsatsu -. Jōhō no kagaku to gijutsu, 52(6), 339–342.
Nelson, L. K. (2020). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research, 49(1), 3–42.
Rheinberger, H.-J. (2008). Begriffsgeschichte epistemischer Objekte. In E. Müller & F. Schmieder (Eds.), Begriffsgeschichte der Naturwissenschaften: Zur historischen und kulturellen Dimensionnaturwissenschaftlicher Konzepte (pp. 1–9). Berlin: de Gruyter.

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