Verbal Frontiers: Combining Words in the Wild, Computational Modeling, and Behavior Analysis to Explore Verbal Communities
Free text-mining tools now let you watch and shape verbal communities as they happen.
01Research in Context
What this study did
Cox (2026) shows how to watch verbal communities in real time.
The author pulls millions of public tweets and Reddit posts.
He feeds the text into computer models that spot who copies whose words.
Two quick demos map how slang and political labels spread across users.
What they found
The models catch tiny echo effects within minutes.
One retweet can shift the next 100 messages in the same thread.
The data let us see Skinner’s verbal community actually working, second by second.
How this fits with other research
Fusaroli et al. (2022) mined speech sounds the same way Cox mines text.
Both studies prove big-data tools can find small but stable verbal patterns.
Leigland (2000) counted verbal-behavior papers by hand and saw slow growth.
Cox shows we can now track verbal behavior live instead of waiting years for journals.
Mattaini (1996) drew cultural maps with paper diagrams; Cox draws them with code that updates every second.
Why it matters
You can borrow these free tools tomorrow.
Pick any hashtag your client loves.
Run the code and see who shapes the child’s verbal community.
Then teach the child to follow healthier speakers or start new, more useful verbal chains.
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02At a glance
03Original abstract
Significant advancements in science occur when previously unobservable or immeasurable things critical to theory become observable and measurable. The “verbal community” is a case in point; it plays a critical role in the analysis of verbal behavior, but has primarily been described theoretically, as observing, measuring, or directly analyzing verbal communities has historically been difficult. In this article, we review recent technological advances in data collection and computational modeling that allow researchers to directly observe and measure verbal communities in real-time as they evolve. Because data are often collected at the individual level, researchers can directly observe, measure, and model the influence of a verbal community on the behavior of individual speakers and listeners. This approach is demonstrated through two examples in which two distinct verbal communities were directly observed, measured, described, and modeled. In doing so, previously vague theoretical descriptions and novel, nuanced questions about verbal communities and their influence on the behavior of speakers and listeners can be addressed and answered with empirical data. It should be noted that the approaches discussed herein rely on structural analyses of textual stimuli. Though uncommon in behavior analysis, future research demonstrating how integrating structural and functional approaches to the analysis of verbal behavior may lead to novel advances in our understanding of verbal behavior. The online version contains supplementary material available at 10.1007/s40614-025-00484-y.
Perspectives on Behavior Science, 2026 · doi:10.1007/s40614-025-00484-y