2009) using quantitative techniques (e.g., computational statistics, machine learning and complexity) and so-called big data for data mining and simulation modeling (Cioffi-Revilla 2010). Social media is especially important for research into computational social science that investigates questions (Lazer et al. Besides social networking media (e.g., Twitter and Facebook), for convenience, we will also use the term ‘social media’ to encompass really simple syndication (RSS) feeds, blogs, wikis and news, all typically yielding unstructured text and accessible through the web. Social media is defined as web-based and mobile-based Internet applications that allow the creation, access and exchange of user-generated content that is ubiquitously accessible (Kaplan and Haenlein 2010). The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.
#Data analysis methods for surveys code#
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. 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.
#Data analysis methods for surveys how to#
Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. 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. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. 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. Although principally a review, the paper also provides a methodology and a critique of social media tools. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds.
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available.
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