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 and credibility 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.
The main aim of the AI4BigData’20 Special Track is to explore the new frontiers of big data computing for social computing, 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 Social Web.
AI4BigData’20 is organized within FLAIRS-33, the 33rd International FLAIRS Conference: https://www.flairs-33.info/
In cooperation with: Association for the Advancement of Artificial Intelligence: http://aaai.org/