Data Paper Research Scrubbing

It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research.

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They either give superficial access to the raw data or (for non-superficial access) require researchers to program analytics in a language such as Java.

Social media data is clearly the largest, richest and most dynamic evidence base of human behavior, bringing new opportunities to understand individuals, groups and society.

Social media is especially important for research into computational social science that investigates questions (Lazer et al. This has led to numerous data services, tools and analytics platforms.

However, this easy availability of social media data for academic research may change significantly due to commercial pressures. , the tools available to researchers are far from ideal.

In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London.

The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business.

An example is Penn State University biologists (Salathé et al.

) who have developed innovative systems and techniques to track the spread of infectious diseases, with the help of news Web sites, blogs and social media.

Wolfram () used Twitter data to train a Support Vector Regression (SVR) model to predict prices of individual NASDAQ stocks, finding ‘significant advantage’ for forecasting prices 15 min in the future.

In the biosciences, social media is being used to collect data on large cohorts for behavioral change initiatives and impact monitoring, such as tackling smoking and obesity or monitoring diseases.

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