Data Paper Research Scrubbing

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available.

It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds.

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.

Innovative scientists and industry professionals are increasingly finding novel ways of automatically collecting, combining and analyzing this wealth of data.

As discussed, it is important that researchers have access to open-source ‘big’ (social media) data sets and facilities for experimentation.

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.

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.

This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms.

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