Exploiting user-frequency information for mining regionalisms in Argentinian Spanish from Twitter.

The task of detecting regionalisms (expressions or words used in certain regions) has traditionally relied on the use of questionnaires and surveys, heavily depending on the expertise and intuition of the surveyor. The emergence of social media and microblogging services has produced an unprecedented wealth of content (mainly informal text generated by users), opening new opportunities for linguists to extend their studies of language variation. Previous work on the automatic detection of regionalisms depended mostly on word frequencies. In this work, we present a novel metric based on Information Theory that incorporates user frequency. We tested this metric on a corpus of Argentinian Spanish tweets in two ways: via manual annotation of the relevance of the retrieved terms, and also as a feature selection method for geolocation of users. In either case, our metric outperformed other techniques based on word frequency, suggesting that measuring the amount of users that use a word is an informative feature. This tool has helped lexicographers discover several unregistered words of Argentinian Spanish, as well as different meanings assigned to registered words.