  In this paper, we present a set of computational methods to identify the
likeliness of a word being borrowed, based on the signals from social media. In
terms of Spearman correlation coefficient values, our methods perform more than
two times better (nearly 0.62) in predicting the borrowing likeliness compared
to the best performing baseline (nearly 0.26) reported in literature. Based on
this likeliness estimate we asked annotators to re-annotate the language tags
of foreign words in predominantly native contexts. In 88 percent of cases the
annotators felt that the foreign language tag should be replaced by native
language tag, thus indicating a huge scope for improvement of automatic
language identification systems.
