Category Archives: Just For Fun

How Much Will Fit Into This Car

Hot on the heals of the “Old Spice” viral ad campaign comes the fun ads for the Honda “Jazz” car, The series come under the banner “Jazz packing”. They definitely have an Australian feel to them. Once again these pieces show that you do not have to spend a ton of money to get a fun idea on video.

Now that we got that out of the way, the commercial series created by Honda is titled, “How much ____ can you pack in a Jazz?” Each commercial picks a different – and hopefully funny – character and proceeds to see just how many people it can pack into the Jazz.

What is also interesting is that one of the ads is way more popular than the others, which shows that just because you have a viral ad does not make it a cool item that everyone wants to watch.

To date this one has over 89,000 views

To date this one has over 236,000 views

To date this one has over 52,000 views

To date this one has over 35,000 views

Is Twitter Count… do the math

Here is a very interesting piece of research by HP that has the dubious tittle of “influence and Passivity in Social Media” and what is go on with twitter This is not a report for the faint hearted or for the die hard Twitterers and twitter-ets that post millions of short comments every day.


The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message.

An evaluation performed with a 2.5 million user data-set shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We also explicitly demonstrate that high popularity does not necessarily imply high influence and vice-versa.

Later in the paper you will have fun math like this to check out.

For every arce = (i, j)∈E, we define theacceptance rate
byuij =
This value represents the amount of influence that userj accepted from useri normalized by the total influence accepted byj from all users on the network. The acceptance rate can be viewed as the dedi- cation or loyalty userj has to useri. On the other hand, for everye = (j, i)∈E we define therejection rate by
(1−wjk ). Since the value 1−wji is amount of influence that useri rejected fromj, then the valuevji represents the influence that useri rejected from userj nor- malized by the total influence rejected fromj by all users in the network.

Hope you like it because my brain is hurting…

The Happiness Machine

If you plan to do a viral campaign learn from some of the best. Check out this video. It’s an awesome example of how to take a great idea and get millions to watch it on the web. Coke’s original goal was to beef up its digital activation platform.The plan was to release seven different pieces of content, iPhone and social media applications, wallpaper screen savers and a video that we hoped would go viral. “The Happiness Machine” web video started out as just a piece of digital content, a dose of happiness.

Twitter Mood Swings In The USA

Pulse of the Nation shows the U.S. Mood Throughout the Day inferred from Twitter comments. For more details info go here.

Mood Variations

A number of interesting trends can be observed in the data. First, overall daily variations can be seen (first graph), with the early morning and late evening having the highest level of happy tweets. Second, geographic variations can be observed (second graph), with the west coast showing happier tweets in a pattern that is consistently three hours behind the east coast.

About the Data and Visualization

The plots were calculated using over 300 million tweets (Sep 2006 – Aug 2009) collected by MPI-SWS researchers, represented as density-preserving cartograms. This visualization includes both weekdays and weekends; in the future, will we create seperate maps for each. The mood of each tweet was inferred using ANEW word list [1] using the same basic methodology as previous work [2]. County area data were taken from the U.S. Census Bureau, and the base U.S. map was taken from Wikimedia Commons. User locations were inferred using the Google Maps API, and mapped into counties using PostGIS and U.S. county maps from the U.S. National Atlas. Mood colors were selected using Color Brewer 2.