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High Performance Network Architectures for Data Intensive Computing Read chapter Read first chapter

2011 | OriginalPaper | Chapter

24. Information Quality and Relevance in Large-Scale Social Information Systems

Author : Munmun De Choudhury

Published in: Handbook of Data Intensive Computing

Publisher: Springer New York

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Abstract

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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previous chapter Design Space Exploration for Efficient Data Intensive Computing on SoCs
next chapter Geospatial Data Management with Terrafly
Footnotes
10
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.
 
11
Note that we do not make apriori assumptions about what value of the diversity parameter is more desirable for the content selection task. Instead, diversity is a parameter in our experimental design, and we provide discussions on how the choice of its value affects the end-user’s perception of information consumption.
 
12
Although our proposed content selection technique can generate tweet sets of any given size, we considered sets of a reasonably small size (ten items) in our experimental design. The goal was to ensure that while going through the user study and evaluating different sets, the end-user participant was not overwhelmed by the quantity of information presented.
 
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Metadata
Title
Information Quality and Relevance in Large-Scale Social Information Systems
Author
Munmun De Choudhury
Copyright Year
2011
Publisher
Springer New York
Book
Handbook of Data Intensive Computing

Print ISBN: 978-1-4614-1414-8

Electronic ISBN: 978-1-4614-1415-5

Copyright Year: 2011

DOI
https://doi.org/10.1007/978-1-4614-1415-5_24

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