Scaling in Words on Twitter
This provides insights into language generation processes on social media, but is incremental as it applies known scaling concepts to Twitter data.
The study analyzed scaling relations in Twitter data across U.S. cities, finding slightly superlinear scaling of tweet and word volume with city population, and that most words show super- or sublinear scaling sensitive to city size, with explanations based on word meaning.
Scaling properties of language are a useful tool for understanding generative processes in texts. We investigate the scaling relations in citywise Twitter corpora coming from the Metropolitan and Micropolitan Statistical Areas of the United States. We observe a slightly superlinear urban scaling with the city population for the total volume of the tweets and words created in a city. We then find that a certain core vocabulary follows the scaling relationship of that of the bulk text, but most words are sensitive to city size, exhibiting a super- or a sublinear urban scaling. For both regimes we can offer a plausible explanation based on the meaning of the words. We also show that the parameters for Zipf's law and Heaps law differ on Twitter from that of other texts, and that the exponent of Zipf's law changes with city size.