Data Paper Research Scrubbing

Innovative scientists and industry professionals are increasingly finding novel ways of automatically collecting, combining and analyzing this wealth of data.Naturally, doing justice to these pioneering social media applications in a few paragraphs is challenging.Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services.

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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.

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.

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.

Retail companies use social media to harness their brand awareness, product/customer service improvement, advertising/marketing strategies, network structure analysis, news propagation and even fraud detection.

In finance, social media is used for measuring market sentiment and news data is used for trading. () measured sentiment of random sample of Twitter data, finding that Dow Jones Industrial Average (DJIA) prices are correlated with the Twitter sentiment 2–3 days earlier with 87.6 percent accuracy.

Computational social science applications include: monitoring public responses to announcements, speeches and events especially political comments and initiatives; insights into community behavior; social media polling of (hard to contact) groups; early detection of emerging events, as with Twitter. () use movie review comments to study the effect of various approaches in extracting text features on the accuracy of four machine learning methods—Naive Bayes, Decision Trees, Maximum Entropy and K-Means clustering.

Lastly, Karabulut (The two major impediments to using social media for academic research are firstly access to comprehensive data sets and secondly tools that allow ‘deep’ data analysis without the need to be able to program in a language such as Java.

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