Content is not King
you hear this all the time from marketing professionals who swear by the power of providing and sharing great content, and what it can do for businesses, government, and people. I agree with it – yes content is important, but network analysis really tells us a slightly different story. The story is that structure is often times superior to content.
Let’s unpack the meaning behind that first. what I mean by structure is network structure – things like tweets, re-tweets, likes, shares, follows, and then more private data (such as meta data) are one type of commonly accepted network structure known as social structure. Content isn’t only what is being said in blogs or articles, but all attributes belonging to a node. In organizational setting I would consider the number of years of experience, or whether someone has a college degree as “content”.
Through having conducted many online and offline network studies, allowing my clients and I to see and analyze the relationships between people in any given network, I can tell you with great certainty that the large majority of time there is no content being collected about those relationships. Often times all you have to do is check who the person is that holds the network together to find what that portion of the network is all about. (see the obama-ryan social experiment under the networks tab).
I think the reason marketers spend so much time telling everyone that content is king is because there is no clear easy to understand alternative. If someone gave you a big flashy speech about how content is not king and that social structure is, and then in the same breath told you that there is no real easy way of measuring social structure, do you think you would be happy with that? the answer is an emphatic no. This is because in the last couple of decades there has been a powerful trend of over-simplifying knowledge for professionals while they attend conferences, or seek out consulting advice.
The evidence is overwhelming. All content is organized into some structure. Books are organized into structure in a library, employees are organized into teams, groups, departments, and divisions, countries into political affiliations, trade circles, and alliances, and businesses into industries, sectors, and supply chains.
What more than 80 years of research into networks has told us is that content if unorganized somehow becomes structures. Sometimes through some external party effort, and sometimes as a natural phenomenon occurring from system complexity and governing policies.
Most would agree with that statement – that everything has structure. I think the question for most is whether structure overcomes content. I can list example after example, and research after research, but then this would be a paper and not a blog. Let me just show you one anecdotal example, and then you be the judge.
What you see here is a collection of several thousand retweets during the US presidential election of 2014, plotted use graphing software, and analyzed using R (programming language). Without knowing anything about what was tweets, and retweeted (hashtag healthcare), you can easily see the this particular network’s political divisions.
In the bottom right going to the middle left (diagonal cross-section) you see the democrats or those who lean democratic, and in the top right you find republicans, and those who lean republican. What’s also interesting is the middle segment of those who do not necessarily lean one way or another but are engaging in their own analysis of the ensuing healthcare debate.
You can find the full interactive map in the network section of the site.
Here it is again but with a little more clarity.
As you can see we can easily identify something like political affiliation or interest based on structure alone, and without any sentiment or keyword analysis (these are retweets and replies only). for most business questions that anyone might have, sentiment analysis (which is based on content alone) simply offers no additional value than what is found through structural analysis and visualization.