Twitter User Representation Using Weakly Supervised Graph Embedding
Keywords:Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Social network analysis; communities identification; expertise and authority discovery, Qualitative and quantitative studies of social media, Psychological, personality-based and ethnographic studies of social media
AbstractSocial 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.
How to Cite
Islam, T., & Goldwasser, D. (2022). Twitter User Representation Using Weakly Supervised Graph Embedding. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 358-369. https://doi.org/10.1609/icwsm.v16i1.19298