Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, this rapidly growing access has also increased the diverse information, and characterizing user types to understand people's lifestyle decisions shared in social media is challenging. In this paper, we propose a weakly supervised graph embedding based framework for understanding user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on 'Yoga', 'Keto diet'. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.

翻译：社交媒体平台为用户提供方便的手段,让他们参与关于各种内容的多个在线活动,并创造快速广泛的互动。然而,这种快速增长的接入也增加了多种信息,并且将用户类型定性为了解社交媒体共享的生活方式决定,这具有挑战性。在本文中,我们提议了一个薄弱监督的图表嵌入框架,用于了解用户类型。我们评估用户在利用微弱监督Twitter上有关福祉的微博(重点是“Yoga ” 、“Keto 饮食 ” ) 所学习的知识嵌入。对真实世界数据集的实验表明,拟议的框架超过了检测用户类型的基线。最后,我们从我们的数据集中演示了对不同类型用户(例如从业者与宣传)的数据分析。我们侧重于与生活方式有关的推文(例如瑜伽、Keto),但我们构建用户代表的方法很容易被推广到其他领域。