Editorial: Deep Mining Big Social Data

Excerpt

The internet revolution has made information acquisition easy and cheap so that it has been producing massive Web/social data in our real life. The emergence of big social media has lead researchers to study the possibility of their exploitation in order to identify hidden knowledge. However, a huge number of issues appear in obtained big social data [23, 24, 26, 28]. First, there are incomplete social data due to all kinds of reasons, such as security and private information. Second, the structure of social data is different, including structured data (e.g., social Web data), semi-structured data (e.g., XML data) and unstructured data (e.g., social networks). Third, the Web data are often high-dimensional. However, current computer techniques can only deal with structured, complete and moderate-sized-dimensional data. Moreover, current computer technologies can only mine the basic structure and are not capable of mining their natural complex structure (or deep structure). Hence, there is a huge gap between existing technologies and the real requirements of actual big social data. In this case, deep mining of big social data (such as data preprocessing, deep pattern discovery, pattern fusion, and outlier/noise detection) stands as an interesting promise to relief such a gap [4, 8, 22, 25, 27] . …