The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series

Buurma, Rachel Sagner

doi:10.1177/2053951715610591

Cited by 9 publications

(5 citation statements)

References 9 publications

(6 reference statements)

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“…The use of computational techniques for big data interrogation both demands and allows something different: exploratory analysis, rather than confirmatory analysis where the 'understanding of the problem you are solving, or might solve, is changing as you go' (O'Neill and Schutt, 2014: 34). This brings with it the opportunity to see things in a new way or, even, to see new things (Buurma, 2015).…”

Section: Methodsmentioning

confidence: 99%

Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

Halford¹,

Savage

2017

Sociology

118

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Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Piketty, Putnam and Wilkinson and Pickett that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.

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Section: Methodsmentioning

confidence: 99%

Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

Halford¹,

Savage

2017

Sociology

118

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“…One of the approaches to determining the number of topics is to assume that these six colleges correspond to six distinct topics. With this approach, the analysis goal is to compare the unsupervised topic modelling results based on six topics to the actual document source [ 45 ]. However, LDA considers topics as latent collection of words occurring frequently together and may therefore discover topics or thematic areas not previously considered to be existing in the documents.…”

Section: Methodsmentioning

confidence: 99%

Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya

Muchene¹,

Safari

2021

PLoS ONE

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Unsupervised statistical analysis of unstructured data has gained wide acceptance especially in natural language processing and text mining domains. Topic modelling with Latent Dirichlet Allocation is one such statistical tool that has been successfully applied to synthesize collections of legal, biomedical documents and journalistic topics. We applied a novel two-stage topic modelling approach and illustrated the methodology with data from a collection of published abstracts from the University of Nairobi, Kenya. In the first stage, topic modelling with Latent Dirichlet Allocation was applied to derive the per-document topic probabilities. To more succinctly present the topics, in the second stage, hierarchical clustering with Hellinger distance was applied to derive the final clusters of topics. The analysis showed that dominant research themes in the university include: HIV and malaria research, research on agricultural and veterinary services as well as cross-cutting themes in humanities and social sciences. Further, the use of hierarchical clustering in the second stage reduces the discovered latent topics to clusters of homogeneous topics.

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“…As argued above, figurations are not ‘texts’ to be read, but realities to be seen. Yet just as digital tools may shed new light on literary texts (Buurma, 2015), learning to see and record the reality of sociotechnical figurations may require literary training and can be facilitated by literary tools. With inspiration from earlier use of Kenneth Burke’s notion of ‘poetic interpretation’ in algorithm studies (see Mohr et al., 2015), we introduce Burke’s dramatistic pentad as our particular strategy for identifying the conceptual personae of SusPens.…”

Section: Methodological Considerationsmentioning

confidence: 99%

Algorithms as organizational figuration: The sociotechnical arrangements of a fintech start-up

2021

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Building on critical approaches that understand algorithms in terms of communication, culture and organization, this paper offers the supplementary conceptualization of algorithms as organizational figuration, defined as material and meaningful sociotechnical arrangements that develop in spatiotemporal processes and are shaped by multiple enactments of affordance–agency relations. We develop this conceptualization through a case study of a Danish fintech start-up that uses machine learning to create opportunities for sustainable pensions investments. By way of ethnographic and literary methodology, we provide an in-depth analysis of the dynamic trajectory in and through which the organization gives shape to and takes shape from its key algorithmic tool, mapping the shifting sociotechnical arrangements of the start-up, from its initial search for a viable business model through the development of the algorithm to the public launch of its product. On this basis, we argue that conceptualizing algorithms as organizational figuration enables us to detail not only what algorithms do but also what they are.

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The fictionality of topic modeling: Machine reading Anthony Trollope's Barsetshire series

Cited by 9 publications

References 9 publications

Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

Two-stage topic modelling of scientific publications: A case study of University of Nairobi, Kenya

Algorithms as organizational figuration: The sociotechnical arrangements of a fintech start-up

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