Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information

TL;DR

This paper explores the causal relationship between yoga practice and happiness on Twitter by analyzing textual and temporal data, using neural networks and transfer learning to identify users for whom yoga influences happiness.

Contribution

It introduces a joint embedding model with attention mechanisms and a transfer learning approach to analyze social media data for yoga-related happiness causality.

Findings

1447 users show yoga Granger-causes happiness

Proposed models effectively measure yoga activity and emotional state

Demonstrates causal link between yoga practice and happiness on social media

Abstract

Although yoga is a multi-component practice to hone the body and mind and be known to reduce anxiety and depression, there is still a gap in understanding people's emotional state related to yoga in social media. In this study, we investigate the causal relationship between practicing yoga and being happy by incorporating textual and temporal information of users using Granger causality. To find out causal features from the text, we measure two variables (i) Yoga activity level based on content analysis and (ii) Happiness level based on emotional state. To understand users' yoga activity, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by leveraging users' social and textual information. For measuring the emotional state of yoga users (target domain), we suggest a transfer learning approach to transfer knowledge from an attention-based neural network model trained on a source domain. Our experiment on Twitter dataset demonstrates that there are 1447 users where "yoga Granger-causes happiness".