Abstract
Purpose: Attention deficit hyperactivity disorder (ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs.
Design/methodology/approach: Tweets matching “my ADHD” (n=58,893) and 99 other conditions (n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.
Findings: The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image. Research limitations: This is a single case study and the results may differ for other topics.
Practical implications: Health professionals should be sensitive to patients’ needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.
Originality/value: Word association thematic analysis can give new insights into the (self-reported) patient perspective.
Design/methodology/approach: Tweets matching “my ADHD” (n=58,893) and 99 other conditions (n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.
Findings: The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image. Research limitations: This is a single case study and the results may differ for other topics.
Practical implications: Health professionals should be sensitive to patients’ needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.
Originality/value: Word association thematic analysis can give new insights into the (self-reported) patient perspective.
Original language | English |
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Pages (from-to) | 13–34 |
Number of pages | 22 |
Journal | Journal of Data and Information Science |
Volume | 6 |
Issue number | 1 |
DOIs | |
Publication status | Published - 7 Dec 2020 |