Task Preferences across Languages on Community Question Answering Platforms

TL;DR

This study analyzes how user task preferences vary across different languages on community Q&A platforms, revealing significant differences in task popularity and trends among linguistic groups.

Contribution

Introduces a multilingual entity-embedding model to quantify and analyze task preferences and trends across diverse linguistic communities on Q&A platforms.

Findings

Substantial variance in task preferences across languages

Distinct trends in task popularity over time among communities

Insights for personalized content curation for non-English users

Abstract

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.