This is a guest post from Marshall Sponder, author of the newly released Social Media Analytics.
Recently, I was asked if Social Analytics sheds light on the nature of online events like the Netflix Outrage, which happened a few weeks ago as customers voiced extreme disappointment at Netflix for imposing a $6 a month increase for customers who still wanted DVD delivery. Not being a Netflix subscriber, I really don’t have a position on this, but I think Social Analytics can work with “Pulse” data according to Douglas Hubbard, who authored a book on the science of harnessing internet buzz to track threats and opportunities. I picked up Hubbard’s book and it nicely fits into my own framework on analytics tracking I put forward in Social Media Analytics.
While many people seemed outraged at the changes (judging by comments left of the Netflix Facebook page), the Technorati article mentioned that no one actually showed up at Netflix headquarters to protest the change. As a result, a lingering question emerges on how much spikes in social media activity represent normal sentiment of interested individuals vs. manipulated sentiment of a few that is then spread around virally, possibly taking a life of its own.
Was the response to the Netflix Outrage was likely to be “endogenous” (meaning a few people inspired the response, which then spread virally) because no one showed up at Netflix’s office to protest, it could have been “exogenous” (a spontaneous response from several individuals who were unrelated, happening at the same exact moment) if we examined the data with the right platforms and mythologies (which I cover in my book).
However, I did not try to answer the Netflix question; instead, I looked at what Social Media Analytics tells us about an exogenous event that happened Monday, August 15th, when flash flooding in New York and New England dominated online conversation in the area for much of the day.
The real challenge of working with “unstructured data” of social media is settling with the presentation of information that is shown to us. Platforms we use to gather data shape the results we get and can report on; by using a different platform, we end up getting different results. As a result, we always run the risk of letting social analytics platforms dominate our vision vs. using platforms to get the information we want to know.
The idea came to me to look at events this way started from a GigaOM post suggesting Some Trending Topics are More Equal Than Others; exogenous trends might be considered more “natural” than endogenous trends as several people in New York City remarked about the rain probably did not know each other online or offline (network nodal analysis can be used to prove it one way or the other). Perhaps the only thing that online commenters are likely to have had in common is being in the same relative location (NYC) at the same time.
The tools to track events to this level of granularity (parts of a day) are not available in most of social analytics platforms I have worked with, but Radian6 Insights has the necessary breakdown to analyze events such as the storm today or the Netflix outrage (where I examined Twitter only).
While each social media analytics platform will shape reporting, users often fail to use the strongest capabilities that are present in that platform; in Radian6, there is a way to find the most engaging content, something that I talk a lot about in Social Media Analytics.
When we look at events, we need to place influencers in context with what is happening – it’s not necessary how many followers someone has on Twitter – but how contextually relevant and well placed the individual is – certainly DiMartino is “weather” influential in this context; though he’s hardly Ashton Kutcher, he doesn’t need to be.
To wrap up, the nature of an event is a complex intertwining of witnesses — many voices, of which a few stand out, but many stories are worth looking at, if we only know how to examine the information. To finish up, I’ll quote from a recent interview I gave where I expressed my opinion about influence.
Take it up step, the Moon is a 4 times smaller than the Earth, and yet, in its strategic position in relation to the Earth, the Moon is of equal importance to the Sun, a body of energy that is 416 times larger than the Moon. Did you know that 72 million Moons can fit within the Sun? Perspective is so important in determining true influence, and there is no platform on that market today that can even partially account for the differences in strategic location and context to make influence mapping something to bank on.
That’s definitely a lot to think about when considering what Social Analytics can bring to the analysis of events.
Marshall Sponder is a Web analytics and SEO/SEM specialist with expertise in market research, social media, networking, and public relations. As both an in-house team leader and consultant, he has used sophisticated analysis to optimize the social media marketing efforts of companies and brands including IBM, Monster, Porter Novelli, WCG, Gillette, Pfizer, Warner Brothers, Laughing Cow, The New York Times, and Havana Central. Sponder is a board member emeritus at the Web Analytics Association, a member of the Search Engine Marketing Professionals Organization (SEMPO), and a member of the Certified Institute of Public Relations Social Media Measurement Study Group (CIPR).
Great blog post. Thanks Mr. Marshall Sponder for the Awesome post
Endogenous vs. exogenous events – a great way to think about events and influence. One takeaway might be to classify events this way. Also, I assume you would be able to tell what kind of event it is is by looking at RTs. Other ways to discern?
First thank you very much for sharing this kind of post Mr. Marshall sponder.
This article is have so much potential to think about ”endogenous” and ”exogenous” events.
Really great work done by author of Social Media Analytics Mr. Marshall sponder.
I recently ordered this book via Flipcart and was impressed by the information that book had to offer. Wow, I never knew that there was so much to analytic of social media.