Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information

TL;DR

This paper introduces a joint embedding model that fuses social and textual data from Twitter to classify user types and motivations related to yoga, advancing understanding of lifestyle choices through social media analysis.

Contribution

It presents a novel neural network with attention mechanism for multiview social and textual data fusion to analyze user activities and motivations.

Findings

Effective classification of yoga practitioners and promoters.

Successful identification of user motivations such as health, spirituality, or social engagement.

Demonstrated model's utility on Twitter data for lifestyle analysis.

Abstract

Leveraging social media data to understand people's lifestyle choices is an exciting domain to explore but requires a multiview formulation of the data. In this paper, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by incorporating social and textual information of users to understand their activities and motivations. We use well-being related tweets from Twitter, focusing on 'Yoga'. We demonstrate our model on two downstream tasks: (i) finding user type such as either practitioner or promotional (promoting yoga studio/gym), other; (ii) finding user motivation i.e. health benefit, spirituality, love to tweet/retweet about yoga but do not practice yoga.