Social media has changed the way people communicate. Millions of people over the world use social media to share information and make connections. Anyone with Internet access can explain their experiences in a video, give their opinion about a fact, or show their photos to millions of other users. This has ledSocial Media Analytics(SMA) to an important growth over last year due to the amount of data shared. Gohfar F. Khan's defines SMA as "the art and science of extracting valuable hidden insights from vast amounts of semistructured an unstructured social media data to enable informed and insightful decision making". Different techniques have been created for analysing opinions towards a product, predicting elections results, studying how fake news spread through social networks. This has made the areas that work in this field very diverse: computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. The increasing interest in the analysis and pattern extraction from Social media information, has given rise a growing interest in various areas of research, not only those related to computer science, but in others such as physics, psychology, or marketing among others. From Computer Science, and other related disciplines, it can be found a wide number of contributions related to Clustering, Graph mining, Community Finding, Natural Language Processing, Entity disambiguation, Information Fusion, Sentiment Analysis, Opinion Mining, or Recommender Systems, to mention only few, which apply their methods and algorithms into real and complex Social Media domains. Some of the current challenges in the area of SMA involve Big Data analytics (data gathering, pre-processing, etc.), Information Fusion, Scalability, Online and Streaming SNA systems, Statistical modelling for large networks, Pattern modelling and extraction, or Visualization.
Therefore, this special issue will be focused on:
1) the application of advanced data science and artificial intelligence techniques for knowledge extraction from social networks, to discuss new models and applications, futures trends and challenges on this area, and
2) the practical use of machine learning, soft computing, computational intelligence, big data, or natural language processing techniques, among others, and their application over complex social media-based domains (as can be seen at the main topics of this special issue).
Topicsappropriate for this special Issue include, but are not necessarily limited to:
Analysis of covert networks, Dark Web
Anomaly detection in social network evolution
Application of social network analysis and mining (e.g. Marketing, Polarization and Radicalization, etc.)
Big Social Mining
Community discovery and analysis in social networks
Community embedding
Clustering and Graph mining algorithms for Social Media
Cybercrime and Social Networks
Data models for social networks and Social Media
Dynamic Community finding and discovery
Entity disambiguation
Evolution of communities/patterns on Social Media
Impact of social networks on recommendations systems
Information acquisition and establishment of social relations
Information fusion in Social Media
Intelligent data analysis in Social Media
Large-scale graph algorithms for social network analysis
Natural language understanding for Social Media
Network formation and evolution
Pattern representation and modelling for Social Networks
Pattern analysis for Social Networks
Personalization for search and for social interaction
Scalability of social networking
Search algorithms for Social Networks
Sentiment Analysis and Opinion Mining in Social Media