Now that my ReTweet mapping system is functioning, I'm able to start compiling more granular data on the actual dynamics of the spread of ReTweet streams.

First, I'll start with some simple averages. For the first 3 numbers -depth, users and Tweets, I'm looking at entire ReTweet streams, that is the whole tree, starting with the original Tweet and all of the subsequent ReTweets. What we see here is that the large majority of ReTweet streams only contain 2 levels of depth, that is ReTweets of the initial Tweet do not themselves produce further ReTweets. They also tend to only include two participating users (the original Tweeter and the ReTweeter) and two individual Tweets. From this, we begin to understand that most RT streams are merely one user ReTweeting another, and never go any further.

Averages

The next data point I'm looking at is the ReTweets per Follower (RTpF) ratio for the users involved in streams I've indexed (just under 20,000 users). The graph below shows the distribution of RTpF in the top 9000 most followed users in my database, I've graphed the actual distribution line in blue, with a 30-point moving average over it in black.

Here we see that while most users had an RTpF of under 1% in my dataset, some users showed much larger ratios, possibly indicating that there are a class of users who are more "ReTweetable" than others. In the future, as I have more data indexed, I plan to release a list of those users with the highest RTpF ratios.

To explore the followers to ReTweet issue a bit more, I then analyzed correlations between followers and stream depth and total Tweets. I used two follower numbers, the first is the number of followers the "root" level of the stream had, that is how many people were potentially exposed to the "seed" Tweet. The second follower number is the combined total of followers of every user who participated in each stream.

While we must remain cautious not to assume a causal relationship between these numbers, it does become clear that there is no significant correlation between either follower number and the depth of a stream. There is on the other hand a significant, though weak, positive correlation between the number of users exposed to a Tweet and the number of times it was ReTweeted. What this (and the distribution graph above) tells me is that while users who have more followers get ReTweeted more often, the number of followers plays a less-than-expected role in predicting how widely something is ReTweeted. I expect to find that the actual content of Tweets explains more of its "ReTweetability".

Correlations

The last data point I looked at in this stage of research are average reproduction rates, that is how many ReTweets in turn trigger further ReTweeting. Of those streams with 2 or more levels, only 7.57% eventually gain an additional level, yet, of those streams with 3 or more levels, nearly 11.5% grow another level. This trend continues out to the 5th level (I did index some streams with more than 5 levels, but not enough to generate any significant data). The more levels a ReTweet stream has, the more it is likely to accumulate.

What this may indicate is that social proof plays a role in a user's decision to ReTweet. The more users a Twitterer sees ReTweet something, the more likely they are themselves to ReTweet it. Another factor in the decision to ReTweet that this data point (as well as the previously noted higher occurrence rate of the "please" call-to-action) may be highlighting is that when the act ReTweeting is called to a user's attention, they may be more likely to ReTweet.

Reproduction Rates by Depth

Look for more posts on this subject, as I'll be developing more functionality into my ReTweet tools and I'll also start investigating content-based correlations, that is what are the factors of the content of a Tweet that make it more or less likely to be ReTweeted.

In my Viral Content Sharing Survey I uncovered a bunch of information that could prove useful to you if you're trying to go viral via Twitter.

First, understand that as gregarious early-adopters Twitter users a very infectious vector to seed your viral content into. Twitter users tend to share content more often and with more people that the average user (in both one-to-one and especially in one-to-many ways). This means that it's probably a good idea to Tweet your new content, and include "Tweet this" buttons to make sharing easier.

Next we can look at where Twitter users tend to share content from. Here we see that they prefer spreading content from blogs and social news sites (rather than mainstream news, a favorite of non-Twitter users). If you want your content to go viral, don't do a press release or use a PR agency; post it to your blog and get it submitted to a social news site.

Lastly, determine what kind of content they like to share most often. The data says that compared to non-Twitter users (who share mostly humor) Twitter users prefer sharing news, opinion and even instructional content.

For data on more user profiles, check out the rest of my Viral Content Sharing Survey.

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Another day, another viral campaign. This time, the mystery fun is brought to us by Nokia. Visiting the page will playback a video outlining the woes of three people who have lost their phones, highlighting the idea that their phone "knows everything" about them. It then switch to the countdown shown above. They've even given all three of the video's stars - Anna, Jade, and Luca - Facebook accounts, complete with fake friends and Wall posts.

Anyone want to take a stab at what they'll announce? The obvious bet would be the phone shown in the countdown - but that seems a bit anticlimactic, and makes the whole omniscient phone aspect kind of pointless. My guess? Some sort of remote wipe/data management set-up aimed at the general consumer. Lose your phone? Take all the naughty stuff off via our handy web app!

Crunch Network: CrunchBoard because it's time for you to find a new Job2.0

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MINI JOHN COOPER WORKS viral video "XTreme Catapulting" nominated:

http://www.viralvideoaward.com/extreme-catapulting/

Vote! Vote! Vote! Vote!

Über 500 Videos aus 34 Ländern wurden für die von Spreeblick unterstützten, im Rahmen des 24. internationalen Kurzfilmfestival 08 stattfindenden viral video awards eingereicht, und auch wenn bestimmt wahnsinnig spannendes Material dabei war, bin ich doch über die Maßen dankbar, nicht zum Vorauswahl-Kommitee gehört zu haben.
Jetzt aber stehen überschaubare 24 Filme zur Wahl, für die ich bei der Preisverleihung am 7. November im Roten Salon meinen Daumen rauf- oder runterdrehen werde.

Vorlegen dürft ihr: Vom 6.10. bis zum 6.11. liegt die Entscheidung beim Publikum.

So go check it out, and let me know if you have any term addition or definition edit suggestions.

And a bunch of people helped me out with this including (but not limited to, if I forgot you, drop me a line and I'll add you) Nick from Heavybag Media and Benj Arriola.

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Mister Tom Klein from Digital Scientists sent me along this interview I did with him as part of the Thomson Reuters Innovation Summit. Despite the fact there's lots of background noise, it does sum up a lot of ideas I often share at speaking events. That was a great event, orchestrated by Digital Scientists, and featuring Guy Kawasaki, Daniel Palestrant, and a few other folks. It's just under 9 minutes long.

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The concept of a reproduction or transmission rate comes from epidemiology. It is the average number of new infections a person infected with a disease will cause. If this number is over 1 the infected population will grow, if it is below one they will shrink in the long term. It assumes a 0% immunity rate in the general population, meaning everyone exposed to the pathogen will become an infectious case themselves.

In memetics and viral marketing this idea is used to indicate the average number of new people that will become "infected" with a meme as a result of a single seed's expressions of the meme. However biological contagions differ from memetic ones in that individuals typically have a higher level of immunity to them, you may be exposed to hundreds or thousands of memes everyday, but you will assimilate and spread some of them.

I propose a more useful model is a multivariate transmission rate, that takes into account a number of variables including separate expression rates (the average number of people a seed will expose to the meme) and assimilation rates (the average number of people who, once exposed to the meme will express it to new people themselves).

This model provides a more realistic and granular description of the processes at work and allows us to study areas (like multiple exposures) were memetic viruses and biological ones vary, and I'll get into exactly that in part 2.

Yesterday I posted on the first of two variables in my proposed multivariate transmission rate formula, expression rate (how many people a seed exposes to a meme) and assimilation rate (how many people exposed to that meme turn into seeds themselves). Today I want to look at two more aspects: multiple exposure assimilation and assimilation threshold.

Multiple exposures to certain memes may increase that meme's assimilation rate. Just hearing an idea from one friend may not catch your attention or allow the meme to be retained in your memory, but when you hear it from two or more friends that could change.

People are exposed to countless new and competing memes everyday and we clearly don't assimilate all of them. As the number of competing (exclusive) memes increases, the number of exposures needed to achieve assimilation increases as well.

The advert appears to show a female model’s breasts smashing through the glass of the bus shelter, thanks to the support of her push-up bra.

The poster â€" which seems at first sight to be a victim of vandalism rather than a marketing stunt - has appeared at sites in Germany.

Last year the company scored a viral Internet hit with a risqué spoof of the cult Cadbury's drumming gorilla advert.

The Wonderbra version featured the enthusiastic and energetic musical efforts of a scantily-clad brunette whose upper-body stability was only maintained thanks to her brassière.

The advert, which had the catchline "Two cups full of joy", was never broadcast on television.

Wonderbra's other recent advertising gimmicks include painting a yellow strip in front of genuine safety lines at Singapore underground stations, to show the safe distance at which Wonderbra wearers will have to stand from the trains.