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Collecting Code-Switched Data from Social Media

We address the problem of mining code-switched data from the web, where code-switching is defined as the tendency of bilinguals to switch between their multiple languages both across and within utterances. We propose a method that identifies data as code-switched in languages L1 and L2 when a language classifier labels the document as language L1 but the document also contains words that can only belong to L2. We apply our method to Twitter data and collect a set of more than 43,000 tweets. We obtain language identifiers for a subset of 8,000 tweets using crowd-sourcing with high inter-annotator agreement and accuracy. We validate our Twitter corpus by comparing it to the Spanish-English corpus of code-switched tweets collected for the EMNLP 2016 Shared Task for Language Identification, in terms of code-switching rates, language composition and amount of code-switch types found in both datasets. We then trained language taggers on both corpora and show that a tagger trained on the EMNLP corpus exhibits a considerable drop in accuracy when tested on the new corpus and a tagger trained on our new corpus achieves very high accuracy when tested on both corpora.