Rationale

As the Web rapidly evolves, Web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing contents, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with a particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Social Web to expand exponentially. The distillation of knowledge and the assessment of its quality from such a large amount of unstructured information, which in the social media context is often diffused without any form of trusted external control, however, are extremely difficult tasks, as the contents of today’s Web are perfectly suitable for human consumption, but remain hardly accessible to machines. Furthermore, faced with this mass of often unverified content, human cognitive abilities are not sufficient to discern reliable information from fake information, and automatic techniques should be provided to tackle the online information credibility issue. By taking into consideration the above mentioned problems, the opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

The main aim of this Special Track is to explore the new frontiers of big data computing for opinion mining, sentiment analysis, and credibility assessment of online information through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.