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Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. / Andreotta, Matthew; Nugroho, Robertus; Hurlstone, Mark J. et al.
In: Behavior Research Methods, Vol. 51, No. 4, 02.04.2019, p. 1766-1781.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Andreotta, M, Nugroho, R, Hurlstone, MJ, Boschetti, F, Farrell, S, Walker, I & Paris, C 2019, 'Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis', Behavior Research Methods, vol. 51, no. 4, pp. 1766-1781. https://doi.org/10.3758/s13428-019-01202-8

APA

Andreotta, M., Nugroho, R., Hurlstone, M. J., Boschetti, F., Farrell, S., Walker, I., & Paris, C. (2019). Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behavior Research Methods, 51(4), 1766-1781. https://doi.org/10.3758/s13428-019-01202-8

Vancouver

Andreotta M, Nugroho R, Hurlstone MJ, Boschetti F, Farrell S, Walker I et al. Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behavior Research Methods. 2019 Apr 2;51(4):1766-1781. doi: 10.3758/s13428-019-01202-8

Author

Andreotta, Matthew ; Nugroho, Robertus ; Hurlstone, Mark J. et al. / Analyzing social media data : A mixed-methods framework combining computational and qualitative text analysis. In: Behavior Research Methods. 2019 ; Vol. 51, No. 4. pp. 1766-1781.

Bibtex

@article{227c1fe60d8947bd8ba532127809ece8,
title = "Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis",
abstract = "To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.",
author = "Matthew Andreotta and Robertus Nugroho and Hurlstone, {Mark J.} and Fabio Boschetti and Simon Farrell and Iain Walker and Cecile Paris",
year = "2019",
month = apr,
day = "2",
doi = "10.3758/s13428-019-01202-8",
language = "English",
volume = "51",
pages = "1766--1781",
journal = "Behavior Research Methods",
issn = "1554-3528",
publisher = "Springer New York LLC",
number = "4",

}

RIS

TY - JOUR

T1 - Analyzing social media data

T2 - A mixed-methods framework combining computational and qualitative text analysis

AU - Andreotta, Matthew

AU - Nugroho, Robertus

AU - Hurlstone, Mark J.

AU - Boschetti, Fabio

AU - Farrell, Simon

AU - Walker, Iain

AU - Paris, Cecile

PY - 2019/4/2

Y1 - 2019/4/2

N2 - To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

AB - To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

U2 - 10.3758/s13428-019-01202-8

DO - 10.3758/s13428-019-01202-8

M3 - Journal article

VL - 51

SP - 1766

EP - 1781

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-3528

IS - 4

ER -