Harvesting Twitter Data for Studying Motor Behavior in Disabled Populations: An Introduction and Tutorial in Python

in Journal of Motor Learning and Development

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Nicholas E. Fears School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA

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https://orcid.org/0000-0001-7081-0015
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Riya Chatterjee School of Kinesiology, University of Michigan, Ann Arbor, MI, USA

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Priscila M. Tamplain Department of Kinesiology, University of Texas at Arlington, Arlington, TX, USA

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https://orcid.org/0000-0003-2713-5733
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Haylie L. Miller School of Kinesiology, University of Michigan, Ann Arbor, MI, USA

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https://orcid.org/0000-0003-4372-1206 *
DOI:
https://doi.org/10.1123/jmld.2023-0006
Keywords:
social media; autism; developmental coordination disorder; dyspraxia
First Published Online:
21 Aug 2023
In Print:
Ahead of Print
Page Range:
1–16
Restricted access

Social media platforms are rich and dynamic spaces where individuals communicate on a person-to-person level and to broader audiences. These platforms provide a wealth of publicly available data that can shed light on the lived experiences of people from numerous clinical populations. Twitter can be used to examine individual expressions and community discussions about specific characteristics (e.g., motor skills, burnout) associated with a diagnostic group. These data are useful for understanding the perspectives of a diverse, international group of self-advocates representing a wide range of clinical populations. Here, we provide a framework for how to harvest data from Twitter through their free, academic researcher application programming interface access using Python, a free, open-source programming language. We also provide a sample data set harvested using this framework and a set of analyses on these data specifically related to motor differences in neurodevelopmental conditions. This framework offers a cost-effective and flexible means of harvesting and analyzing Twitter data. Researchers should utilize these resources to advance our understanding of the lived experiences of clinical populations through social media platforms and to determine the critical questions that are of most importance to improving quality of life.

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