  Code mixing (or code switching) is a common phenomenon observed in
social-media content generated by a linguistically diverse user-base. Studies
show that in the Indian sub-continent, a substantial fraction of social media
posts exhibit code switching. While the difficulties posed by code mixed
documents to further downstream analyses are well-understood, lending
visibility to code mixed documents under certain scenarios may have utility
that has been previously overlooked. For instance, a document written in a
mixture of multiple languages can be partially accessible to a wider audience;
this could be particularly useful if a considerable fraction of the audience
lacks fluency in one of the component languages. In this paper, we provide a
systematic approach to sample code mixed documents leveraging a polyglot
embedding based method that requires minimal supervision. In the context of the
2019 India-Pakistan conflict triggered by the Pulwama terror attack, we
demonstrate an untapped potential of harnessing code mixing for human
well-being: starting from an existing hostility diffusing \emph{hope speech}
classifier solely trained on English documents, code mixed documents are
utilized as a bridge to retrieve \emph{hope speech} content written in a
low-resource but widely used language - Romanized Hindi. Our proposed pipeline
requires minimal supervision and holds promise in substantially reducing web
moderation efforts.
