  Code-mixing or code-switching are the effortless phenomena of natural
switching between two or more languages in a single conversation. Use of a
foreign word in a language; however, does not necessarily mean that the speaker
is code-switching because often languages borrow lexical items from other
languages. If a word is borrowed, it becomes a part of the lexicon of a
language; whereas, during code-switching, the speaker is aware that the
conversation involves foreign words or phrases. Identifying whether a foreign
word used by a bilingual speaker is due to borrowing or code-switching is a
fundamental importance to theories of multilingualism, and an essential
prerequisite towards the development of language and speech technologies for
multilingual communities. In this paper, we present a series of novel
computational methods to identify the borrowed likeliness of a word, based on
the social media signals. We first propose context based clustering method to
sample a set of candidate words from the social media data.Next, we propose
three novel and similar metrics based on the usage of these words by the users
in different tweets; these metrics were used to score and rank the candidate
words indicating their borrowed likeliness. We compare these rankings with a
ground truth ranking constructed through a human judgment experiment. The
Spearman's rank correlation between the two rankings (nearly 0.62 for all the
three metric variants) is more than double the value (0.26) of the most
competitive existing baseline reported in the literature. Some other striking
observations are, (i) the correlation is higher for the ground truth data
elicited from the younger participants (age less than 30) than that from the
older participants, and (ii )those participants who use mixed-language for
tweeting the least, provide the best signals of borrowing.
