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<p>Collecting data on social topics by analyzing verbal behavior on social media is a modern approach to assessing social validity. Given the evolving public perception related to applied behavior analysis (ABA), we sought to gain an objective understanding of online discourse related to the field. In an earlier project, we explored the relevance and sentiment (i.e., positive, negative, neutral) of a stratified sample of Twitter/X posts related to ABA (e.g., #ABA, #BehaviorAnalysis, #appliedbehaviouranalysis) across 11&#xa0;years (2012–2022). Tweets with negative sentiment garnered engagement scores that were about three times higher than those of positive and neutral tweets. However, the nature of the discourse was unknown. In the present study, we extended the results by conducting a content analysis of a subsample of tweets categorized as indicating either a bias toward or against ABA. Tweets supporting ABA focused on promotion, positive effects on recipients, effective procedures, financial support, and ethical practices. Tweets against ABA highlighted harmful effects on recipients, criticisms of specific procedures, ableist assumptions, and general anti-ABA sentiments. The results were interpreted according to social validity and invalidity, and disability studies perspectives. Our findings have meaningful social implications for ABA practitioners, those who access our services, and the general public.</p>

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#ContentAnalysisofABAonTwitter/X: Finding Behavior Analysis’ Heart on Social Media

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Laura E. Mullins,
  • Albert Malkin,
  • Priscilla Burnham Riosa,
  • Allison Kretschmer,
  • Jess Walker

摘要

Collecting data on social topics by analyzing verbal behavior on social media is a modern approach to assessing social validity. Given the evolving public perception related to applied behavior analysis (ABA), we sought to gain an objective understanding of online discourse related to the field. In an earlier project, we explored the relevance and sentiment (i.e., positive, negative, neutral) of a stratified sample of Twitter/X posts related to ABA (e.g., #ABA, #BehaviorAnalysis, #appliedbehaviouranalysis) across 11 years (2012–2022). Tweets with negative sentiment garnered engagement scores that were about three times higher than those of positive and neutral tweets. However, the nature of the discourse was unknown. In the present study, we extended the results by conducting a content analysis of a subsample of tweets categorized as indicating either a bias toward or against ABA. Tweets supporting ABA focused on promotion, positive effects on recipients, effective procedures, financial support, and ethical practices. Tweets against ABA highlighted harmful effects on recipients, criticisms of specific procedures, ableist assumptions, and general anti-ABA sentiments. The results were interpreted according to social validity and invalidity, and disability studies perspectives. Our findings have meaningful social implications for ABA practitioners, those who access our services, and the general public.