Twitter User Representation Using Weakly Supervised Graph Embedding
This work addresses the challenge of understanding user lifestyles on social media, but it is incremental as it applies an existing weakly supervised graph embedding approach to a new domain with specific data.
The paper tackles the problem of characterizing user types on social media by proposing a weakly supervised graph embedding framework, which outperforms baselines in detecting user types like 'Yoga' and 'Keto diet' practitioners on Twitter.
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.