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As the surge of today’s pervasive social applications continues unabatedly, it has greatly expanded our horizons of putting the shared information artifacts to good use. Almost inconceivable scarcely a decade ago, on one hand, it has enabled researchers to study social processes on these systems at extremely large-scales. While on the other, it has streamlined the end user experience in terms of exploring real-time event based information ubiquitously via a variety of devices, almost anytime, anywhere. However, with several terrabytes of such information generated everyday, we are presented with the daunting question: how do we identify those pieces of information that are relevant and interesting? This book chapter sheds light on the significance, challenges associated with this problem domain and presents a case study geared towards addressing these challenges. Finally it identifies the impact of the vision to the larger data intensive computing community.
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The diversity index of a sample population has been widely used by researchers in different areas ranging from economics, ecology and statistics, to measure the differences among members of the population consisting of various types of objects. Although there are a host of measures to estimate such diversity (e.g., species richness, concentration ratio, etc.), the most popular and robust measure by far is Shannon’s entropy based quantification [ 16]. This motivated us to utilize an information theoretic formulation to represent the diversity existing in social information spaces.
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- Information Quality and Relevance in Large-Scale Social Information Systems
Munmun De Choudhury
- Springer New York
- Chapter 24
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