A corpus linguistics study of SMS text messaging

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On learning and representing social meaning in NLP: a sociolinguistic perspective (2021, background): “A different term, such as ‘respellings’, may therefore be more appropriate (Tagg, 2009).”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “The SMS messages in this dataset are extracted from various resources, including 425 SMS from Grum-bletext, 3,375 from NUS SMS Corpus, and 450 SMS ham (not spam) messages from Caroline Ph.D. Thesis [181], respectively.”