“Social media include all the ways people connect to people through computation. Mobile devices, social networks, email, texting, micro-blogging and location sharing are just a few of the many ways people engage in computer mediated collective action. As people link, like, follow, friend, reply, retweet, comment, tag, rate, review, edit, update, and text one another (among other channels) they form collections of connections.”
These collections of connections are capable of containing a varied range of user driven network structures. These network structures can be visualised, analysed, and processed to generate network insights. The network insights are used to obtain valuable information on different aspects of social media functions, structures, variations, and behaviours.
Data mining can help in manipulating social media data more effectively. According to Gundecha and Liu (2012, section 1.1), data mining can be defined as “a process of discovering useful or actionable knowledge in large-scale data.” Given the diversified nature and hugeness of social media data, implementation of data mining techniques has emerged as a coveted alternative to ...
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...is purposeful adaptation of PageRank algorithm.
NodeXL is another kind of algorithmic application. The basic syntax of this algorithm follows that of MS Excel (Bonsignore et al 2009). So, the NodeXL is capable of providing visualisation methods such as charts, tables, etc. In this way, analysis of SNS data is made a lot easier (Bonsignore et al 2009). NodeXL is also capable of suitably adapting its algorithms that can provide useful graphical layouts. These adaptations are aimed at simplifying the social media data analysis tasks. Consequently, operating NodeXL does not call for too many pre-processing activities. It acts like a visual tool for applying MS Excel based syntax and formulas (Hansen, Shneiderman, and Smith 2011; also see Figure – 3). Hence, NodeXL can be used for the purpose of mining social media data even inside a manageable computing infrastructure.
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