  Social media plays a significant role in cross-cultural communication. A vast
amount of this occurs in code-mixed and multilingual form, posing a significant
challenge to Natural Language Processing (NLP) tools for processing such
information, like language identification, topic modeling, and named-entity
recognition. To address this, we introduce a large-scale multilingual, and
multi-topic dataset (MMT) collected from Twitter (1.7 million Tweets),
encompassing 13 coarse-grained and 63 fine-grained topics in the Indian
context. We further annotate a subset of 5,346 tweets from the MMT dataset with
various Indian languages and their code-mixed counterparts. Also, we
demonstrate that the currently existing tools fail to capture the linguistic
diversity in MMT on two downstream tasks, i.e., topic modeling and language
identification. To facilitate future research, we will make the anonymized and
annotated dataset available in the public domain.
