Bridging big data and qualitative methods in the social sciences: A case study of Twitter responses to high profile deaths by suicide

Abstract

With the rise of social media, a vast amount of new primary research material has become available to social scientists, but the sheer volume and variety of this make it difficult to access through the traditional approaches: close reading and nuanced interpretations of manual qualitative coding and analysis. This paper sets out to bridge the gap by developing semi-automated replacements for manual coding through a mixture of crowdsourcing and machine learning, seeded by the development of a careful manual coding scheme from a small sample of data. To show the promise of this approach, we attempt to create a nuanced categorisation of responses on Twitter to several recent high profile deaths by suicide. Through these, we show that it is possible to code automatically across a large dataset to a high degree of accuracy (71%), and discuss the broader possibilities and pitfalls of using Big Data methods for Social Science.

Publication
Online Social Networks and Media
Dmytro Karamshuk
Dmytro Karamshuk
Former Postdoc, now Research Scientist at Facebook Core Data Science.
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