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

Dmytro Karamshuk, Frances Shaw, Julie Brownlie, Nishanth Sastry

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)
223 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)33-43
Number of pages11
JournalOnline Social Networks and Media
Volume1
Early online date17 Apr 2017
DOIs
Publication statusPublished - Jun 2017

Keywords

  • Social media
  • Crowd-sourcing
  • Crowdflower
  • Natural language processing
  • Social science
  • Emotional distress
  • High-profile suicides
  • Public empathy

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