Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model
arXiv:2104.03189 · doi:10.1609/icwsm.v15i1.18057
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.
arXiv commentaccepted at 15th International AAAI Conference on Web and Social Media (ICWSM-2021), 12 pages. Minor changes for camera-ready version
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- Efficient Estimation of Word Representations in Vector Space
- Neural Machine Translation by Jointly Learning to Align and Translate
- Decoupled Weight Decay Regularization
- Longformer: The Long-Document Transformer
- Abusive Language Detection with Graph Convolutional Networks
- Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
- Quantifying Mental Health from Social Media with Neural User Embeddings
- Yoga-Veganism: Correlation Mining of Twitter Health Data
- Overcoming Language Variation in Sentiment Analysis with Social Attention
- Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information