Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

中文翻译：

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社交媒体中的短文学习：回顾

社交网络在其用户的生活中占据着无处不在且无处不在的位置。社交网络用户生成和共享的大量内容为各种学科提供了新的研究机会，包括媒体和传播研究、语言学、社会学、心理学、信息和计算机科学或教育。这种情况与社交媒体数据的持续增长相结合，对内容组织产生了迫切的需求。因此，社会环境中的大规模文本学习任务成为机器学习和数据挖掘中最相关的问题之一。有趣的是，社交媒体数据由于其稀疏、高维和大容量的特点而带来了一些挑战。本次调查回顾了社交媒体数据学习领域，专注于分类和聚类技术，因为它们是最常见的两个学习任务。它不仅回顾了为应对短文本带来的新挑战而开发的新技术，还回顾了如何调整传统技术来克服这些挑战。然后，讨论了社交媒体数据学习的开放问题和研究机会。