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.
翻译:利用社交媒体数据来理解人们的生活方式选择是一个值得探索的令人兴奋的领域,但需要多角度的数据配方。 在本文中,我们提出一个基于将神经网络与关注机制融合起来的联合嵌入模式,将用户的社会和文字信息纳入其中,以了解他们的活动和动机。我们使用推特上有关福祉的推特,重点是“Yoga ” 。我们展示了我们的两个下游任务模式:(一) 找到用户类型,如实践者或宣传者(促进瑜伽工作室/工作室),其他任务;(二) 找到用户的动机,即健康福利、精神、热爱有关瑜伽的推文/retweet(推文/retweet),但不练习瑜伽。