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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteScientists studied spellings such as “duuuude” and “hahahaha” to measure how people stretch letters in informal online writing. Their 2020 study analyzed roughly 100 billion tweets from a 10% sample of Twitter’s stream collected between 2008 and 2016. It proposed tools that could inform language technology—but it did not show that an AI model became more accurate.
What the study examined
Published in PLOS ONE on May 27, 2020, the paper by Tyler J. Gray, Christopher M. Danforth, and Peter Sheridan Dodds is titled “Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings.” It focuses on character-level patterns in informal writing, not on announcing a new commercial AI system.
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The authors analyzed roughly 100 billion tweets in a 10% random sample of Twitter’s gardenhose stream, spanning September 9, 2008, through December 31, 2016. They limited the analysis to tweets labeled English or not labeled with a language. Examples of stretchable words include “heellllp,” “heyyyyy,” “gooooooaaaalll,” and “hahahaha.”
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Elongation can add emphasis, exaggeration, or tone that a standard spelling does not convey. The study turns that visible variation into patterns that can be counted and compared.
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What “stretch” and “balance” mean
- Stretch measures the overall amount of repetition or elongation in a word.
- Balance measures how evenly that elongation is distributed across its characters. Repeating several letters more evenly gives a word higher balance than extending just one character.
These are separate dimensions: two words can contain substantial repetition but distribute it differently. The researchers used balance plots and spelling trees to visualize these patterns and examine how misspellings and mistypings develop.
What this could mean for language technology
The researchers proposed that the measurements and visual tools could support work in language processing, dictionary augmentation, search engines, and other tasks involving sequence construction. Their point is that nonstandard spellings contain structure worth studying, rather than simply being noise to discard.
The University of Vermont research team described the work this way: “We were able to comprehensively collect and count stretched words like ‘gooooooaaaalll’ and ‘hahahaha’, and map them across the two dimensions of overall stretchiness and balance of stretch, while developing new tools that will also aid in their continued linguistic study, and in other areas, such as language processing, augmenting dictionaries, improving search engines, analyzing the construction of sequences, and more.” The statement appears in ScienceDaily’s report on the study.
Did the research make AI better?
No demonstrated improvement is reported in the paper. The study presents measurements and methods with possible applications; it does not report training a current large language model or benchmarking an AI system before and after using them. The headline’s connection to AI is therefore about potential relevance to language technology, not a proven performance gain.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the tweet count does—and does not—represent
The figure of roughly 100 billion refers to the analyzed sample, not every tweet ever posted. It was drawn from a 10% stream sample over the stated 2008–2016 period, with the language restriction described above. The original individual tweets cannot be redistributed because of Twitter API terms, which limits access to the messages themselves.
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