Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

Authors

  • Tunazzina Islam Department of Computer Science, Purdue University, West Lafayette, Indiana 47907
  • Dan Goldwasser Department of Computer Science, Purdue University, West Lafayette, Indiana 47907

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, Psychological, personality-based and ethnographic studies of social media, Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health

Abstract

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.

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Published

2021-05-22

How to Cite

Islam, T., & Goldwasser, D. (2021). Analysis of Twitter Users’ Lifestyle Choices using Joint Embedding Model. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 242-253. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18057