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Conducting Semantic-Based Network Analyses from Social Media Data: Extracted Insights about the Data Leakage Movement
Abstract
Network analysis is widely used to mine social media. This involves both the study of structural metadata (information about information) and the related contents (the textual messaging, the related imagery, videos, URLs, and others). A semantic-based network analysis relies on the analysis of relationships between words and phrases (as meaningful concepts), and this approach may be applied effectively to social media data to extract insights. To gain a sense of how this might work, a trending topic of the day was chosen (namely, the free-information and data leakage movement) to see what might be illuminated using this semantic-based network analysis, an open-source technology, NodeXL, and access to multiple social media platforms. Three types of networks are extracted: (1) conversations (#hashtag microblogging networks on Twitter; #eventgraphs on Twitter; and keyword searches on Twitter; (2) contents (video networks on YouTube, related tags networks on Flickr, and article networks on Wikipedia; and (3) user accounts on Twitter, YouTube, Flickr, and Wikipedia.
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