Abstract
Naturalistic observation of verbal behavior on social media is a method of gathering data on the acceptability of topics of social interest. In other words, online social opinion may be a modern-day measure of social validity. We sought to gain an objective understanding of online discourse related to the field of applied behavior analysis (ABA). We analyzed Twitter posts related to ABA (e.g., #ABA, #BehaviorAnalysis, #appliedbehavioranalysis). Our initial sample consisted of 119,911 tweets from 2012 to 2022. We selected a random subset (n = 11,000) for further analysis using a stratified sampling procedure to ensure that tweets across years were adequately represented. Two observers were trained to code tweets for relevance and sentiment toward the field. A total of 5,408 relevant tweets were identified and analyzed, with an arithmetic mean of 492 tweets per year. Tweets were categorized as having neutral (51.41%), positive (43.81%), or negative (4.79%) sentiment. Negative sentiment tweets received approximately three times higher engagement scores compared to positive and neutral tweets. Positive sentiment tweets commonly used hashtags related to special education, therapy, behavior analysis, autism, and specific individuals. Negative sentiment tweets focused on the harmful effects of ABA, disability, variations of ABA, and promoting alternatives to ABA. Our results suggest that there is a small but vocal minority that has the potential to shape the narrative on ABA. We suggest a path forward for behavior analysts in the study of the online discourse on ABA.
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Data Availability
The Python code to download Tweets from X/Twitter and the datasets generated and analyzed during the current study are available in the Open Science Framework repository, https://osf.io/58yse/ ; https://doi.org/10.17605/OSF.IO/58YSE
Notes
In July 2023, Twitter changed its name to X, and tweets and retweets, under X, are referred to as posts/re-posts. At the time of data gathering and analysis, the platform was known as Twitter, hence, when referring to our methods and analysis, we will use the name Twitter, and label posts as tweets throughout.
This access excludes tweets judged to be spam by Twitter algorithms and tweets deleted by their authors.
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The authors thank David J. Cox and Marc Lanovaz for their helpful guidance on this project, as well as Qi Wan for her assistance with data collection.
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Malkin, A., Riosa, P.B., Mullins, L. et al. #ExploratoryAnalysisOfSentimentTowardABAonTwitter. Behav Analysis Practice (2024). https://doi.org/10.1007/s40617-024-00929-x
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DOI: https://doi.org/10.1007/s40617-024-00929-x