Conceptualizing Big Social Data

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• 作者：Ekaterina Olshannikova ; Thomas Olsson ; Jukka Huhtamäki…
• 关键词：Big Social Data ; Social Big Data ; Digital human ; Conceptualization ; Social Data ; Social media ; Computational social science ; Social computing ; Classification ; Big Social Data analysis
• 刊名：Journal of Big Data
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：4
• 期：1
• 全文大小：1500KB
• 刊物类别：Database Management; Information Storage and Retrieval; Data Mining and Knowledge Discovery; Computa
• 刊物主题：Database Management; Information Storage and Retrieval; Data Mining and Knowledge Discovery; Computational Science and Engineering; Mathematical Applications in Computer Science; Communications Engine
• 出版者：Springer International Publishing
• ISSN：2196-1115
• 卷排序：4

文摘

The popularity of social media and computer-mediated communication has resulted in high-volume and highly semantic data about digital social interactions. This constantly accumulating data has been termed as Big Social Data or Social Big Data, and various visions about how to utilize that have been presented. However, as relatively new concepts, there are no solid and commonly agreed definitions of them. We argue that the emerging research field around these concepts would benefit from understanding about the very substance of the concept and the different viewpoints to it. With our review of earlier research, we highlight various perspectives to this multi-disciplinary field and point out conceptual gaps, the diversity of perspectives and lack of consensus in what Big Social Data means. Based on detailed analysis of related work and earlier conceptualizations, we propose a synthesized definition of the term, as well as outline the types of data that Big Social Data covers. With this, we aim to foster future research activities around this intriguing, yet untapped type of Big Data.