Paper Info
Title
Short-text learning in social media: a review
Abstract
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.
Year
DOI
Venue
2019
10.1017/S0269888919000018
KNOWLEDGE ENGINEERING REVIEW
DocType
Volume
ISSN
Journal
34
0269-8889
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Antonela Tommasel1258.87
Daniela Godoy250238.22