A variety of data to be processed continues to witness a quick increase. Effective management and analysis of large-scale data poses an interesting but critical challenge. Parallel processing frameworks and cloud computing were presented to store and process such problems. Various data generated constantly by users are consisted of textual, multimedia and usage records/logs, social interaction data also included. Multitudinous data sources spring up for knowledge mining, targets tracking or relationships analysis which ultilize the techniques of data mining. Nevertheless, a significant challenge emerges when facing the data, text, web, and social network mining introduced by these massive, heterogeneous and non-synchronous data. For example, generic data modeling and massive data process. To obtain efficient, accurate, trustworthy, distributed and parallel mining results is becoming increasingly crucial since future success of applications significantly depends on and benefits from data mining and its intelligence.

In this decade, data, text, web, and social network mining (DTWSM) has been paid an increasing attention and gained serious studies towards being successfully applied in the practice of the data incentive applications and services. This forum is founded to promote closer exchange between researchers and professionals from worldwide academia and industry for showcasing, discussing, and reviewing the whole spectrum of technological opportunities, challenges, solutions, and emerging applications in this research area.

Topics of interest include, but are not limited to:

Information indexing and retrieval
Theoretic foundations of heterogeneous data mining
Mining heterogeneous/multi-source data
User behavior analysis
Mining spatial and temporal data
Mining unstructured and semi-structured data
Mining social networks
Mining high dimensional data
Mining uncertain data
Mining imbalanced data
Mining dynamic/streaming data
Personalization, privacy and security
Opinion mining and sentiment analysis
Human, domain, organizational and social factors in data mining