Charting Mob Emotions using “We Feel Fine”
August 19th, 2006

I found out about “We Feel Fine” program from SEOBOOK and took at the program and wrote about it in WebMetricsGuru and ArtNYC yesterday, focusing on the art persepctive for ArtNYC and the Metrics perspective in Webmetricsguru. But I also wanted to write about the SmartMobby aspects of the program here in SmartMobs.

We Feel Fine is a program that spawns a Javascript Applet and allows you to explore the Mass Feelings of Mobs of people in a location, gender, age, whatever; it’s interface needs work but it’s an interesting idea and worth taking a look at. The program uses 6 different ways of displaying information about Mass Feelings, with the one most SmartMobby – called “Mobs”. Here’s the information about the emotion of “Mobs”.

Mobs, the fourth movement, consists of five smaller movements, each of which utilizes a self-organizing particle system to configure its shape, color, distribution and physics to best express the different zeitgeists of: feeling, gender, age, weather, and geographical location.

Mobs (Feeling) displays the most common feelings in the sample population. In this movement, the particles self-organize into rows of shared feelings. The rows are sorted by the number of particles they contain, and the particles within each row are sorted by the length of the sentence that each particle contains. The rows are colored to inherit the chosen color of the feeling they represent. Any particle can be clicked to reveal the sentence within.

I feel fine.JPG

Mobs (Gender) displays the gender breakdown of the sample population. The male particles turn blue and form a giant male symbol. The female particles turn pink and form a giant female symbol. The particles with unknown gender retain their assigned feeling color and form a giant question mark.

Mobs (Age) displays the age breakdown, in ten year increments, of the sample population. The particles form a standard bar chart, showing the number of feelings from each age range. The particles with unknown age form a giant question mark.

Mobs (Weather) displays the weather breakdown of the sample population. All weather conditions are pared down to four weather types: sunny, cloudy, rainy, and snowy. The screen divides into four columns, each representing a single weather type. The particles move to the appropriate column, and assume the color and motion behavior that best expresses their weather. Sunny particles turn yellow, and swirl around quickly in a thick circle, reminiscent of the sun. Cloudy particles turn light blue, and float along gently, as if on a breezy day. Rainy particles turn gray and fall down fast as if in a thunder storm. Snowy particles turn white and tumble around as if in a snow flurry. Any particle can be clicked to reveal the sentence inside. The particles with unknown weather form a giant question mark.

Mobs (Location) displays the geographical breakdown of the sample population. A world map appears, colored dark gray. The particles then move to the point on the map that corresponds to the geographical location of their author. The particles with unknown location form a giant question mark.

I played with the program for a while last night and wrote down how I thought it could be improved in WebMetricsGuru, but I think it could also have SmartMobby applications – although it does not provide collective action or mobile communications – perhaps something like this should, or could include it in the future.

It’s also interesting to read about the technology of We Feel Fine and the method of collecting data and categorizing it my age, sex and location as well as by feeling.

When the applet is first opened, the initial dataset consists of the most recent 1,500 feelings collected by our system. The applet’s panel can then be used to arbitrarily specify different populations, constrained by any combination of:

Feeling (happy, sad, depressed, etc.)
Age (in ten year increments – 20s, 30s, etc.)
Gender (male or female)
Weather (sunny, cloudy, rainy, or snowy)
Location (country, state, and/or city)
Date (year, month, and/or day)

Obviously, the more specific the population, the fewer feelings it will contain, and the less significant any associated statistical computations will be. For example, asking for feelings from “20 year old males in Bagdhad Iraq when it’s rainy” might yield few or no feelings, whereas, asking for feelings from “20 year olds in New York City” would result in a larger number of feelings

I would be curious to know what people think of an application like this – what would make it better?

Links: SEOBOOK, WebMetricsGuru, ArtNYC, Joel Manning

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