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Sampling Public Sentiment Using Related Tags (and User-Created Content) Networks from Social Media Platforms
Abstract
The broad popularity of social content-sharing sites like Flickr and YouTube have enabled the public to access a variety of photographs and videos on a wide range of topics. In addition to these resources, some new capabilities in multiple software programs enable the extraction of related tags networks from these collections. Related tags networks are relational contents built on the descriptive metadata created by the creators of the digital contents. This chapter offers some insights on how to understand public sentiment (inferentially and analytically) from related tags and content networks from social media platforms. This indirect approach contributes to Open-Source Intelligence (OSINT) with nuanced information (and some pretty tight limits about assertions and generalizability). The software tools explored for related tags data extractions include Network Overview, Discovery, and Exploration for Excel (NodeXL) (an open-source graph visualization tool which is an add-in to Microsoft Excel), NCapture in NVivo 10 (a commercial qualitative data analysis tool), and Maltego Tungsten (a commercial penetration-testing Internet-network-extraction tool formerly known as Maltego Radium).
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