Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information
This research aims to understand the lifestyle choices and motivations of Twitter users interested in yoga, which could be useful for marketers or public health initiatives.
This paper proposes a joint embedding model using neural networks and attention mechanisms to analyze Twitter data related to 'Yoga'. The model classifies users into types (practitioner, promotional, other) and identifies their motivations (health benefit, spirituality, love to tweet/retweet but not practice).
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.