English verb regularization in books and tweets

Gray, Tyler J.; Reagan, Andrew J.; Dodds, Peter Sheridan; Danforth, Christopher M.

doi:10.1371/journal.pone.0209651

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…Their main focus is the (ir)regularization of past-tense verbs (e.g. the change from irregular snuck to regular sneaked), a topic that has been of some interest (Lieberman et al 2007;Cuskley et al 2014;Gray et al 2018). They also investigate the change in periphrastic do (say not that!…”

Section: Introductionmentioning

confidence: 99%

Challenges in detecting evolutionary forces in language change using diachronic corpora

Karjus

Blythe

Kirby

et al. 2020

Glossa: A Journal of General Linguistics

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Newberry et al. (Detecting evolutionary forces in language change, Nature 551, 2017) tackle an important but difficult problem in linguistics, the testing of selective theories of language change against a null model of drift. Having applied a test from population genetics (the Frequency Increment Test) to a number of relevant examples, they suggest stochasticity has a previously under-appreciated role in language evolution. We replicate their results and find that while the overall observation holds, results produced by this approach on individual time series can be sensitive to how the corpus is organized into temporal segments (binning). Furthermore, we use a large set of simulations in conjunction with binning to systematically explore the range of applicability of the Frequency Increment Test. We conclude that care should be exercised with interpreting results of tests like the Frequency Increment Test on individual series, given the researcher degrees of freedom available when applying the test to corpus data, and fundamental differences between genetic and linguistic data. Our findings have implications for selection testing and temporal binning in general, as well as demonstrating the usefulness of simulations for evaluating methods newly introduced to the field.

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Section: Introductionmentioning

confidence: 99%

Challenges in detecting evolutionary forces in language change using diachronic corpora

Karjus

Blythe

Kirby

et al. 2020

Glossa: A Journal of General Linguistics

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“…These tools may be particularly useful for researchers in philosophy, English, and other disciplines who are interested in quantifying aspects of discourse. Researchers in the digital humanities have recently made a number of interesting advances (8,9,13,45,46), and with the right tools, hopefully scholars can begin to quantify features of culture only dreamed about previously.…”

Section: Discussionmentioning

confidence: 99%

How quantifying the shape of stories predicts their success

Toubia

Berger

Eliashberg

2021

Proc. Natl. Acad. Sci. U.S.A.

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Narratives, and other forms of discourse, are powerful vehicles for informing, entertaining, and making sense of the world. But while everyday language often describes discourse as moving quickly or slowly, covering a lot of ground, or going in circles, little work has actually quantified such movements or examined whether they are beneficial. To fill this gap, we use several state-of-the-art natural language-processing and machine-learning techniques to represent texts as sequences of points in a latent, high-dimensional semantic space. We construct a simple set of measures to quantify features of this semantic path, apply them to thousands of texts from a variety of domains (i.e., movies, TV shows, and academic papers), and examine whether and how they are linked to success (e.g., the number of citations a paper receives). Our results highlight some important cross-domain differences and provide a general framework that can be applied to study many types of discourse. The findings shed light on why things become popular and how natural language processing can provide insight into cultural success.

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“…Spring Forward is defined as the instantaneous clock adjustment from 2 a.m. to 3 a.m. on the second Sunday of March each year. We included tweets in the study if the user who created the tweet reported living in the U.S. in their bio, or if the tweet was geo-tagged to a GPS coordinate within the U.S. [44]. With these conditions, we ended up selecting approximately 7% of the messages in the Decahose random sample for analysis [45].…”

Section: Datamentioning

confidence: 99%

The Sleep Loss Insult of Spring Daylight Savings in the US Is Observable in Twitter Activity

Linnell¹,

Arnold²,

Alshaabi³

et al. 2021

Preprint

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Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use \Spring Forward" events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs 15 to 30 minutes later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that the window of sleep opportunity is compressed in Twitter data, revealing Spring Forward behavioral change.

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English verb regularization in books and tweets

Cited by 14 publications

References 13 publications

Challenges in detecting evolutionary forces in language change using diachronic corpora

Challenges in detecting evolutionary forces in language change using diachronic corpora

How quantifying the shape of stories predicts their success

The Sleep Loss Insult of Spring Daylight Savings in the US Is Observable in Twitter Activity

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