Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model
This work addresses the problem of analyzing user lifestyle choices on social media for researchers or marketers, but it is incremental as it builds on existing multiview representation learning methods.
The paper tackled the problem of understanding Twitter users' lifestyle choices by developing a joint embedding model that incorporates social and textual information to learn contextualized user representations, applied to tweets about Yoga and Keto diet, resulting in performance improvements in both domains.
Multiview representation learning of data can help construct coherent and contextualized users' representations on social media. This paper suggests a joint embedding model, incorporating users' social and textual information to learn contextualized user representations used for understanding their lifestyle choices. We apply our model to tweets related to two lifestyle activities, `Yoga' and `Keto diet' and use it to analyze users' activity type and motivation. We explain the data collection and annotation process in detail and provide an in-depth analysis of users from different classes based on their Twitter content. Our experiments show that our model results in performance improvements in both domains.