  Language varies across users and their interested fields in social media
data: words authored by a user across his/her interests may have different
meanings (e.g., cool) or sentiments (e.g., fast). However, most of the existing
methods to train user embeddings ignore the variations across user interests,
such as product and movie categories (e.g., drama vs. action). In this study,
we treat the user interest as domains and empirically examine how the user
language can vary across the user factor in three English social media
datasets. We then propose a user embedding model to account for the language
variability of user interests via a multitask learning framework. The model
learns user language and its variations without human supervision. While
existing work mainly evaluated the user embedding by extrinsic tasks, we
propose an intrinsic evaluation via clustering and evaluate user embeddings by
an extrinsic task, text classification. The experiments on the three
English-language social media datasets show that our proposed approach can
generally outperform baselines via adapting the user factor.
