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This is really unacceptable, but it is indeed a compromising situation. Nevertheless, without a compromise of this kind, social media research cannot be enriched and popularized. The compromise involves a restrictive integration between social media analysis and data mining techniques. The basic philosophy underlying this socio-technological amalgamation can be stated as ‘What we get after we get something or everything?’ Breaking apart terminological barriers, this something-everything problem can be regarded as a new variety of requirements engineering issue. At one side, experts are having loads of data generated by social media platforms. On the other side, experts are wondering how data mining can be used optimally to understand and analyse social media trends. While doing the both tasks simultaneously, experts are now trying to restrict the scope of large-scale data mining applications inside the realm of social media. This conception, known approximately as social media mining, may help in drawing an eventual but erroneous conclusion. This potential conclusion is that data mining is best suited for organizing, managing, analyzing, and exploiting social media data only! Can the scientific world afford that the computer programmers of future almost solely dedicate themselves to social media mining research and development? In order to understand the risks of such an approach, please have a look to the following academic works and an informative weblog:
 Kaplan, Andreas M.; Haenlein, Michael (2010). “Users of the world, unite! The challenges and opportunities of social media”. Business Horizons 53 (1)
 Barbier, Geoffrey; Feng, Zhuo; Gundecha, Pritam; Liu, Huan (2013). “Provenance Data in Social Media”. Synthesis Lectures on Data Mining and Knowledge Discovery.
 Why Data Mining Is the Next Frontier for Social Media Marketing