#ExploratoryAnalysisOfSentimentTowardABAonTwitter
Negative ABA tweets are rare but travel far—track and reply early to protect your practice.
01Research in Context
What this study did
Malkin et al. (2024) scraped Twitter for every tweet that talked about ABA. They kept 5,408 posts written from 2012 to 2022. A computer program read each tweet and tagged it as positive, neutral, or negative.
What they found
Only 5 out of every 100 tweets were negative. Yet those few angry posts got three times more likes, retweets, and replies than the happy ones. Almost half the tweets were neutral, and 44 percent were positive.
How this fits with other research
Guinness et al. (2022) mined BACB test data and found distance programs graduate more BCBAs even though their pass rates are lower. Both papers show that louder numbers do not always tell the whole story.
DPMacFarland et al. (2025) and Osório et al. (2025) mapped ABA language tools and found only two checklists exist. The Twitter study adds a new layer: public feeling about those tools and the field itself.
Laguna et al. (2025) used AI on baby cries; Malkin used AI on tweets. Both show machine learning can spot patterns humans would miss, but one looks at toddlers and the other at the whole internet.
Why it matters
You can’t fix a rumor you never hear. Set a free alert for "ABA" on Twitter, TikTok, and Instagram. Once a week, spend five minutes reading the top negative post. Reply with calm facts or a link to a parent-friendly article. One short post can stop a bad story from spreading to your next client.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →Create a Google alert for "ABA therapy" and reply to one negative tweet with a helpful link.
02At a glance
03Original 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.
Behavior Analysis in Practice, 2024 · doi:10.1007/s40617-024-00929-x