Elsevier Neurocomputing Journal Special Issue on
Social Media Analytics and Learning
Aim and Scope:
Online social platforms have developed to a virtual world where users record, share/broadcast, and comment on various snapshots of their real lives and various facets of the real world, leading to an explosive proliferation of social media on the Internet. For example, Flickr and YouTube offer places for media sharing among users; and Facebook, Twitter, and Instagram enable users to connect with their social audiences through media content (e.g., images/videos). The availability of massive and heterogeneous social media data, consisting of media content, users, social context, geo-locations, and other metadata, have created numerous new research opportunities and challenges. Consequently, social media analytic and learning has become an increasingly attractive research direction. More and more research efforts have been dedicated to key issues therein, such as analytics and learning techniques towards understanding social media, social media analytics tools and systems, knowledge mining from social media, as well as social network modeling, etc.
This special issue seeks contributions reporting novel solutions, models, theories, or systems regarding social media analytics and learning. Topics of interest include but not limited to: