The social nature of Web 2.0 leads to the unprecedented growth of social media sites such as discussion forums, product review sites, microblogging, social networking, and social curation. Existing research in social media data mining has focused on techniques for extracting information for specific applications from separate social media sources.

The mobile network and the Internet of Things are transforming what it means to be social online. Humans, everyday objects, and smart devices interact and form an intelligent social network that is a highly adaptive complex system. Assisted by personal devices, people can access real-time traffic, weather, and news event information and exchange such information through social interaction and form communities dynamically. The rich user- and device-generated data and user interactions generate complex social big data that is different from classical structured attribute-value data. The data objects take various forms including unstructured text, geotagged data objects, and data object streams. The social networks formed from interactions among data objects also carry rich information for analyzing user behavior. Such complex social big data calls for cross disciplinary research from data mining, machine learning, pervasive and ubiquitous computing, network science, and computational social science.

We seek contributions to advance our knowledge in social big data mining and analytics and extend the knowledge to related disciplines. We especially welcome high original research articles as well as review articles including methodological papers that address the data complexity and application papers that promote wider and deeper applications of social media data.

Potential topics include but are not limited to the following:

• Personal device and content integrated social data mining
• Mining dynamic complex social networks of humans and devices
• Mining heterogeneous streams of social media data objects from humans and devices
• Spatiotemporal analysis of social data and social networks
• Privacy preserving social data mining
• Social influence and community discovery in dynamic social networks
• Social data mining for community-based recommendation and other applications
• Trust and information credibility analysis of social media data
• Mining for user social influence and communities in complex social networks of humans and devices
• Mining social data for smart cities and smart nations
• Humans as sensors for event detection and disaster management
• Sentiment analysis and opinion mining for social good
• Detection of opinion spam, illicit behavior, and anomalies in social media
• Social media data mining for public health and healthcare