The emergence of social network led to the boost of user generated content which consequently gave rise to fields such as data mining and web mining. The social network also gave rise to the new phenomena called social media which empowered the users towards citizen journalism where users can choose to act as content providers instead of more content consumers. The social networking websites such as Facebook, Twitter, LinkedIn, Flickr, and Weibo etc, are a few examples of such empowerment. Furthermore, these social media platforms provide new opportunities to explore the user behavior which could benefit for various applications related to economy, marketing, education, business, medicine, etc. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from large-scale social media data.
SMMA-2018 is the next edition of SMMA-2017 (Exeter,UK), SMMA-2016 (Toulouse, France), SMMA-2015 (Liverpool, UK) and aims to discuss the theories and methodologies from different disciplines such as computer science, data mining, machine learning, social network analysis, network science, sociology, and statistics in order to provide conceptual insights on mining social media data.
We invite researchers and practitioners namely from communities of artificial intelligence, data mining, and social network analysis to share their ideas, innovations, research achievements and solutions in fostering the advancement of intelligent data analytics and management of social media data. We solicit original, unpublished, and innovative research work on applying any intelligent technologies and methods to all aspects around the theme of this symposium. The symposium is co-located with HPCC-2018, the 18th IEEE International Conference on High Performance Computing and Communications.
Topics of interest include, but are not limited to:
* Fundamentals of social computing
* Statistical modeling of large networks
* Communities discovery and analysis in large-scale social networks
* Large-scale graph algorithms for social network analysis
* Reputation, trust, privacy and security in social networks
* Expert systems and decision-making for social media data
* Recommendation systems and marketing
* Methods for tie strength or link prediction
* Methods for extracting and understanding user and group behavior
* Crowdsourcing and collective intelligence
* Other issues related to various social computing applications and case studies.