This may sound odd, but making a user action easier for everyone is not always a good idea.  An overly simple explanation for this is that the less effort it is to do something, the less significant the action becomes.  That doesn’t mean that everything should be hard, it means there’s an optimal level of difficulty v. reward in social behavior.  I’d rather not see Twitter encouraging a particular kind of social connection until the structures it supports are better understood.  Has anybody really shown the value of retweeting in creating strong social networks?  If so, it it clear that the API would tend to further strengthen them?  I fear that the API is motivated by a more naive assumption – people are doing this anyway, so let’s make it easier.  While that assumption is fine for things like soap, it isn’t right for social behavior.

I’ve been doing social media analytics for a long time.  One of the things I’m always trying to measure is how much energy went into a particular user behavior or action.  For example, a message that contains more original words took more energy than a shorter one.  A message that quotes more than one person takes more energy than one that quotes just one person.  A message that contains a URL probably took more energy than one that doesn’t.  If the URL is unique in the medium, it probably took more energy to create than a URL that already existed.

If the effect of the retweet API is to make retweeting so simple that the act of retweeting loses much of its significance, that’s a net loss.  More people might retweet, but less of them will be deeply engaged.  Social systems should never have the goal of getting everyone to the same level of engagement.  It is human nature for some to be opinion leaders, but they don’t easily emerge when “playing the game” is made easy for everyone.  Unfortunately, the idea of getting as many people as possible to be as active as possible is a deeply engrained habit in the media industry.  But any successful community manager or analyst will tell you that it is far more important to pay attention and nurture the “core community” that exists in any social network.

The sweet spot for ease of retweeting lies somewhere between it being so hard that only the most committed users do it (and the current manual method is far better than that) and being so easy that everybody essenially “votes” on everything, which would be bad.  Even though that sounds like democracy, it is really demarchy.  Seen any successful demarchies?  I didn’t think so.

I’m not so sure that Twitter isn’t already in the sweet spot and the API is going to drive it away from there.  I suspect that Twitter and those who analyze it haven’t had enough time to really figure out how it will fit into the social networking ecosystem in the long run, so any decsions about this are premature.  I’d rather see them continue make the social network easier to analyze, not just for the sake of analytics, but because the results of analytics are getting fed back into the network, which makes the network smarter and smarter.

I think of my methods of analyzing social media in three categories:

• Traffic analysis
• Social network analysis
• Linguistic/text analysis

Traffic analysis includes today’s typical web analytics and a bit more. Beyond the basics, it is the idea of measuring hidden events by analyzing visible patterns they cause. This is like figuring out how big a rock tossed into a pond is by measuring the waves it produces. A simple social example is to look for correlations between the number of people participating in a discussion and who is participating. If you find that there are certain people whose presence correlates to higher activity, you might infer that they are provoking greater participation. Or the cause might be the other way around – certain people only enter the fray when things get hot.

Social network analysis assumes, reasonably, that groups of people interacting on-line organize themselves into roles such as opinion leaders, connectors, lurkers and so forth, and seeks to identify who’s who in the network. Law enforcement and intelligence agencies have used these techniques to solve problems such as figuring out whose telephones to tap. If there are 200 people in a crime syndicate, but resources only allow you to tap 10 phones, how do you choose which 10? One answer is to find the 10 people who, as a group, talk to the greatest number of the 200. That way, odds are you’ll hear a bit of what everybody is talking about. What’s more, those 10 are likely to be highly influential.

Linguistic/text analysis aims to figure out what people are talking about and their attitudes (sentiment) toward those topics. This is the hardest kind of analysis because language is complex and ambiguous and computers are extremely stupid when it comes to language. However, just looking at how language changes over time – which words or phrases are growing in popularity – can reveal quite a bit.

I don’t use these three approaches in isolation. The language part is so challenging that I have relied on traffic and social network analysis to narrow down the interesting people to a handful, which makes linguistic and text analysis computationally simpler. That is really no different from the telephone tapping example, except that law enforcement generally still relies on humans to figure out what is really going on. Computers can recognize words and phrases increasingly well, but even at Fort Meade, I’m fairly sure they still need people to truly make sense of language.