Earlier this year I read a paper called “Modeling Blog Dynamics” in which they propose a method of modeling the spread of links through the blogosphere using zero-crossing random walks and exploitation vs. exploration applied to a logical flowchart model:
The authors suggested that the model could be used in influence maximization algorithms which aim to identify key, influential individuals in a given social network for the purposes of viral marketing. I was intrigued by the possibilities and have been tossing around a possible flowchart model of how individuals decide to ReTweet specific Tweets since reading that paper. Here’s my first attempt:
There are three steps in the process where a marketer can increase the chances of a specific Tweet being ReTweeted. The first step indicates that a user must be following the sender of the target Tweet; the second step means that they must actually see the Tweet in question (try to imagine what percentage of your friend’s timeline you actually see). Step three is where the user must find some motivation to ReTweet it.
Maximizing the number of followers the Tweet’s original sender has is fairly straightforward, and most of my Science of ReTweets data has explored the ReTweet motivation percentage. I had not put much effort into analyzing statistics around the attention problem, but I’ve begun to.
Because there is no way to exactly measure what percentage of followers will actually read a given Tweet, the next best metric we have is click through percentages, so that is what I’ve been working with. You can expect to see more work to that end in the next few weeks.
My work has been concentrated on maximizing the contagiousness of ideas, whereas much of the aforementioned academic work focuses on the people involved in spreading ideas. So you can also expect to see me advance the concepts of “ReTweetability” I began a few months ago with the purpose of identifying influential users.
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October 26th, 2009 at 12:32 pm
Wow! This is impressive!!!! Thanks for sharing such a brilliant post.
October 26th, 2009 at 10:05 pm
Hi Dan,
Great work.
I'm not sure what I'm sharing is correct, but I just want to share.
Even when you don't follow a person, there are Re-tweets. This takes places during a general search or a #tag search. When readers find the link or tweet interesting, they Re-tweet it. So how do you incorporate this aspect of Re-tweet into the Re-Tweet Decision Model.
October 26th, 2009 at 11:32 pm
Hey Dan,
There's another metric that should be put in this flowchart and that is how influential your followers are. An example to that is I once tweeted a funny thought. That was re-tweeted by 3 followers who were heavily followed. Result was that within minutes my tweet was RTed by 14 other people who don't follow me but follow those 3 followers.
November 17th, 2009 at 6:43 am
hey dan
I really like your ReTweet Decision model. I'm wondering if you have any data on the attention % or motivation % – Have you calculated any probabilities or trends? It would be great to be able to say on average, this is how many people are likely to see your Tweet.
I would think that motivation would be esp. difficult to calculate bc (as you have also stated) it's really the content of the tweet that influences people to ReTweet it.
thanks!
z
November 23rd, 2009 at 6:01 am
hey Pratik,
I thought about the same thing when i was reading this post. if you have follower that is strong enough,(has lot of followers) and your tweet get some % of attention of that follower and with certain % of motivation for RTing your Tweet then it is better to have few good followers than thousands of crappy ones.
What do you think of that?
cheers
November 23rd, 2009 at 6:31 am
hey zoeDisco,
i'm using few services for determining how my followers feel, do they tweet about good things, about work or bad things like death, killing…
i think that psychic state of followers is very valuable to know and it could be implemented in RT dynamics approach.
Example:
if you find that your followers are tweets most of the time about business, chances for getting RT of info tweet is very likely to be high, and vice verse.
Furthermore with one service you can see impact of your tweets on your followers. I saw that i lost 11 followers after i tweeted info that my company has got exclusive right for one software( obviously my followers thought that tweet was commercial) but then i've got 11 followers after i tweeted about OCZ RAM that i bought.
November 27th, 2009 at 12:59 am
The first decision stage of the flowchart excludes the many other instances how a reweet could occur even if a person is not being followed.
As SimZaolly rightly pointed out, it could happen via a search result, whether it be a #tag search or a general search.
It could happen if a person is not being followed but “listed” using a list. Also, it could be from reading a blog post with the TweetMeme button. It could also be published on retweet directories like Retweetist.com. Let's not forget also that a growing number of Tweeter users are on mobile devices. So the RT could happen from mobile applications like Mashable's ReTweet app.
All of these instance can happen even if the person retweeting is not following you.
December 2nd, 2009 at 9:35 pm
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Thanks
Micheal,
December 3rd, 2009 at 5:35 am
Websites are always helpful in one way or the other, The authors suggested that the model could be used in influence maximization algorithms which aim to identify key, influential individuals in a given social network for the purposes of viral marketing. I was intrigued by the possibilities and have been tossing around a possible flowchart model of how individuals decide to ReTweet specific Tweets since reading that paper. Here’s my first attempt: anyways, instant loans a good way to get started to renovate your dreams into the world of reality.
Thanks
Micheal,
December 26th, 2009 at 3:01 am
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