07/06/2021

Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

Tunazzina Islam, Dan Goldwasser

Keywords: Psychological, personality-based and ethnographic studies of social media, Social network analysis, communities identification, expertise and authority discovery, Subjectivity in textual data, sentiment analysis, polarity/opinion identification and extract

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.

 0
 0
 0
 0
This is an embedded video. Talk and the respective paper are published at ICWSM 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers