Can you become influential on Twitter merely by Tweeting a lot?

A bit more than a month ago, I asked the question: Can you become influential on Twitter, and get a high Klout Score, merely by Tweeting a lot?

To test this, I set up an experiment, which involves four Twitter bots that automatically tweet the output of the Unix fortune command-line application. Fortune randomly outputs mildly humorous quotes, and was often used on Unix to produce a ‘welcome message of the day’ upon login.

The four bots Tweet once every minute, once every five minutes, once every fifteen minutes and once every thirty minutes respectively. They are completely anonymous, have no avatars or custom user profiles set, and do not follow anyone.

Now, after 80 days of running the experiment (Jules Verne style), there’s a set of pretty hot data available.

The Data (the good)

Let’s start off by simply plotting the amount of followers for each bot against time:

Follower accumulation over time

We can clearly see from this graph (quite surprisingly), that each bot accumulated followers linearly. Also, it seems the more they tweeted, the steeper the follower accumulation rate is, without any drop off, even for the bot that tweets every minute.

This brings us to a question: Can these graphs in some way be normalized? Surely the bot that Tweets at the annoying rate of once every minute, should get fewer followers per tweet as the one that Tweets at a more acceptable once every 30 minutes?

Let’s normalize the data by plotting the amount of followers against the amount of Tweets, thereby literally measuring the amount of followers per Tweet:

Followers per Tweet

The scale is a bit awkward, but it seems that these bots are all more or less following the same slope, in other words, by the time the once every 30 minutes bot has tweeted as much as the once a minute bot, it will have the same amount of followers.

Let’s test this assumption, by plotting the curves over time again, including an amplification factor equal to the amount of minutes that lapse between Tweets. That means, we assume the once every 5 minutes bot would have had 5 times more followers if it Tweeted once every minute, etc:

Normalized Amount of Followers over time

Transient fluctuations aside, These curves really do seem to follow roughly the same path – linearly upwards.

That means, the more you Tweet, the more followers you get. Period. It doesn’t matter how often you tweet, you gain an equal amount of followers for every time you Tweet.

The Followers (the bad)

Now, on that bombshell … time for a sobering revelation:

Looking at the followers of these bots, many of them seem to be bots themselves (there are quite a few real people who attempt conversations with them, but they are in the minority). Most of these bots get triggered by keywords present in our bots’ Tweets, and then follow and retweet our bots’ Tweets. A good example is @BurroughsBot, which retweets Tweets that match the search term William Burroughs.

At this point I turned to Klout (which, incidentally, is the actual reason for setting up this experiment in the first place). Surely Klout should be able to make sense of this robotic mess (like Google does with link farms), shouldn’t it?

The Klout Scores (the ugly)

For all practical purposes though, no matter how I look at it, Klout seems to be broken.

Consider the following Klout scores, for the four bots:

Klout Score: Bot 1Klout score for ‘once a minute’ bot

Klout Score: Bot 2Klout Score for ‘once every 5 minutes’ bot

Klout Score: Bot 3Klout Score for ‘once every 15 minutes’ bot

Klout Score: Bot 4Klout Score for ‘once every 30 minutes’ bot

What’s wrong with this picture? To start off with, it should not really be possible for a bot to reach a Klout Score of 50 within 80 days merely by Tweeting random (yet entertaining) rubbish every minute, should it?

24 hours after the above klout scores were sampled, I took another set of samples, just to be sure:

Klout Score 2: Bot 1Klout Score for ‘once every minute’ bot

Klout Score 2: Bot 2Klout Score for ‘once every 5 minutes’ bot

Klout Score 2: Bot 3Klout Score for ‘once every 15 minutes’ bot

Klout Score 2: Bot 4Klout Score for ‘once every 30 minutes’ bot

Roughly the same result, except for huge fluctuations in transient metrics (see True Reach for Bot 1), which also seems a bit suspect. We can’t say for sure without knowledge of Klout’s exact algorithm.

The fact is, though, no matter how you look at it, unless Klout updates this aspect of their algorithm, in another 80 days Bot 1 could very well have the same Klout Score as @scobleizer!

Taking into account that many Twitter clients (like Hootsuite) and filter applications (like Datasift) are using Klout as a trusted way of filtering tweets, it means Klout will have to up their game on this one to stay in the game.

Or else, we might just be run by machines sooner than we think!

This post originally appeared on the blog of social media agency RAAK