Social media research is still behind social media marketing in terms of getting past the hype.  Clearly there’s some overselling going on and more education is needed about how and when to effectively use social media data.  Some sales folks even go as far as suggesting social media listening can replace market research as a way to save money – without having the background or unbiased perspective needed to make such a suggestion. 

It’s time for researchers to have an open dialogue about social media data – warts and all.  What biases exist?  What steps are necessary to put the data in a truly usable form?  What are the best applications for social media analysis?  How can we best integrate it with other data sources?  I’m not going to try and tackle all these questions in this blog, but hopefully I can help stimulate discussion over time.

To put things in perspective, one must consider that typically only a fraction of social media chatter is worthwhile and relevant to your specific objectives. Keep in mind that the topic of interest for your social media analysis has a huge impact on how many “sound bites” you have to work with.  As you are pulling data, it can be a challenge to “disambiguate” (i.e., remove irrelevant chatter) and, in some instances, almost impossible.  Another challenge is that social media data is largely unstructured. Automatic coding isn’t optimal – especially if you plan to integrate the results with other data sources. 

Despite these challenges, there is no denying that it’s a valuable data source.  Having the ability to learn from chatter that is occurring naturally online and applying state-of-the-art technology to aggregating and analyzing this data is powerful stuff.   Social media analytic tools and text analytics are always evolving.   But even with the best social media listening tools and analysts available, social media listening cannot and should not be applied across all situations.  NO analytic tool or technique is a one-size-fits-all solution.   

Let’s put social media analysis in perspective across all of the tools, techniques, and data sources we have to work with.   Exciting things are on the horizon, but for now, let’s not expect (or promise) more than social media data can deliver.

Cathy is CMB’s social media research maven dedicated to an “eyes wide open” approach to social media research and its practical application and integration with other data sources. Follow her on Twitter at @VirtualMR

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