  Language in social media is mostly driven by new words and spellings that are
constantly entering the lexicon thereby polluting it and resulting in high
deviation from the formal written version. The primary entities of such
language are the out-of-vocabulary (OOV) words. In this paper, we study various
sociolinguistic properties of the OOV words and propose a classification model
to categorize them into at least six categories. We achieve 81.26% accuracy
with high precision and recall. We observe that the content features are the
most discriminative ones followed by lexical and context features.
