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Twitter contains an abundance of linguistic data from the real world. Weexamine Twitter for user-generated content in low-resource languages such aslocal Indonesian. For NLP to work in Indonesian, it must consider localdialects, geographic context, and regional culture influence Indonesianlanguages. This paper identifies the problems we faced when constructing aLocal Indonesian NLP dataset. Furthermore, we are developing a framework forcreating, collecting, and classifying Local Indonesian datasets for NLP. Usingtwitter's geolocation tool for automatic annotating.

Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages