Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model

TL;DR

This paper introduces a joint embedding model that combines social and textual data from Twitter to analyze users' lifestyle choices, improving understanding of their activity types and motivations.

Contribution

The paper presents a novel joint embedding approach that integrates social and textual information for user lifestyle analysis on social media.

Findings

Model improves performance in lifestyle activity classification

Effective in analyzing user motivation and activity types

Provides detailed user content analysis

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.