  Social media platforms provide convenient means for users to participate in
multiple online activities on various contents and create fast widespread
interactions. However, this rapidly growing access has also increased the
diverse information, and characterizing user types to understand people's
lifestyle decisions shared in social media is challenging. In this paper, we
propose a weakly supervised graph embedding based framework for understanding
user types. We evaluate the user embedding learned using weak supervision over
well-being related tweets from Twitter, focusing on 'Yoga', 'Keto diet'.
Experiments on real-world datasets demonstrate that the proposed framework
outperforms the baselines for detecting user types. Finally, we illustrate data
analysis on different types of users (e.g., practitioner vs. promotional) from
our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our
method for constructing user representation readily generalizes to other
domains.

Computation and Language;Artificial Intelligence;Computers and Society;Machine Learning;Social and Information Networks