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

Nicholas E Fears^1,2, Riya Chatterjee¹, Priscila M Tamplain³

¹School of Kinesiology, University of Michigan, Ann Arbor, MI, USA.

Journal of Motor Learning and Development

|January 29, 2024

Summary

Researchers can use Twitter data to understand the lived experiences of diverse clinical populations. This framework details how to collect and analyze social media data for insights into neurodevelopmental conditions and quality of life.

Keywords:

autism developmental coordination disorder dyspraxia social media

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Area of Science:

Computational social science
Digital health
Clinical informatics

Background:

Social media platforms offer rich data on individual experiences within clinical populations.
Publicly available data can illuminate self-advocate perspectives on specific health characteristics.
Understanding lived experiences is crucial for improving quality of life.

Purpose of the Study:

To present a framework for harvesting and analyzing Twitter data for clinical research.
To demonstrate the utility of social media data for understanding neurodevelopmental conditions.
To provide a cost-effective and flexible method for researchers.

Main Methods:

Utilizing Twitter's free academic researcher application programming interface (API).
Employing Python, a free, open-source programming language, for data harvesting and analysis.
Developing a framework applicable to diverse clinical populations and characteristics.

Main Results:

A sample dataset of Twitter data related to motor differences in neurodevelopmental conditions was generated.
The framework facilitates the examination of individual expressions and community discussions.
Analysis revealed insights into lived experiences relevant to specific diagnostic groups.

Conclusions:

Social media data, particularly from Twitter, provides valuable insights into clinical populations.
The proposed framework offers an accessible and efficient method for researchers.
Further research using this framework can enhance understanding and improve quality of life for individuals with clinical conditions.