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

TY - JOUR

T1 - Bridging big data and qualitative methods in the social sciences: A case study of Twitter responses to high profile deaths by suicide

AU - Karamshuk, Dmytro

AU - Shaw, Frances

AU - Brownlie, Julie

AU - Sastry, Nishanth

PY - 2017/6

Y1 - 2017/6

N2 - 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.

AB - 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.

KW - Social media

KW - Crowd-sourcing

KW - Crowdflower

KW - Natural language processing

KW - Social science

KW - Emotional distress

KW - High-profile suicides

KW - Public empathy

U2 - 10.1016/j.osnem.2017.01.002

DO - 10.1016/j.osnem.2017.01.002

M3 - Article

SN - 2468-6964

VL - 1

SP - 33

EP - 43

JO - Online Social Networks and Media

JF - Online Social Networks and Media

ER -