  With the steady emergence of community question answering (CQA) platforms
like Quora, StackExchange, and WikiHow, users now have an unprecedented access
to information on various kind of queries and tasks. Moreover, the rapid
proliferation and localization of these platforms spanning geographic and
linguistic boundaries offer a unique opportunity to study the task requirements
and preferences of users in different socio-linguistic groups. In this study,
we implement an entity-embedding model trained on a large longitudinal dataset
of multi-lingual and task-oriented question-answer pairs to uncover and
quantify the (i) prevalence and distribution of various online tasks across
linguistic communities, and (ii) emerging and receding trends in task
popularity over time in these communities. Our results show that there exists
substantial variance in task preference as well as popularity trends across
linguistic communities on the platform. Findings from this study will help Q&A
platforms better curate and personalize content for non-English users, while
also offering valuable insights to businesses looking to target non-English
speaking communities online.
