ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Social Media Big Data Integration: A New Approach Based on Calibration

12 September 2017, 15:40 – 16:00


Submitted by
Luciana Dalla Valle
Luciana Dalla Valle (Plymouth University), Ron Kenett (KPA Ltd. and University of Turin)
In recent years, the growing availability of huge amounts of information generated in every sector at high speed and in a wide variety of formats is unprecedented. This is known as “big data”. The ability to harness big data, such as social media, is an opportunity to obtain more accurate analyses and improved decision-making in industry, government and many other organizations. On the other hand, handling big data may be challenging and proper data integration is a key dimension in achieving high information quality (Kenett and Shmueli, 2016). In this paper, we apply a data integration approach that calibrates online generated big data with organizational or administrative data using Bayesian networks. The methodology combines different data sources by identifying overlapping links that are used for calibration and enhanced information quality. It expands earlier work by the authors that was focused on integrating official statistics with administrative data and data from various surveys.
We illustrate the application of the methodology with an example of integration between online data from blogs and customer satisfaction surveys. This demonstrates how this methodology enhances the information quality (InfoQ) of a study in four of the InfoQ dimensions: Data Structure, Data Integration, Temporal Relevance and Chronology of Data and Goal.

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