Author Archives: Peter Sheridan Dodds

About Peter Sheridan Dodds

Scientist thinking about stories, language, spreading and contagion, robustness and collapse, complexification, and the Theory of Anything. @peterdodds http://www.uvm.edu/~pdodds

How our storytelling nature means we deeply misunderstand the mechanics of fame (and much else…)

Should the Mona Lisa be our most famous painting?

Was Harry Potter destined to (repeatedly) sweep the globe?

What would happen to everyone and everything famous if we ran the experiment that is our world over again?

Find out why fame is truly unpredictable, how it lives and dies entirely in our social stories, and why “… there is no such thing as fate, only the story of fate” in a current Nautilus Magazine piece by the Computational Story Lab’s co-team leader Peter Dodds:

“Homo Narrativus and the Trouble with Fame: We think that fame is deserved. We are wrong.”  

Nautilus is a new, design-driven publication on science published both online (free) and in print (unfree).  The Nautilus team is creating a beautiful showcase for scientific knowledge, and we encourage you to explore everything they have on offer.

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Filed under networks, prediction, psychology, social phenomena

Tweets and happiness.

Quotidian Twitter verbiage

Relative use of food-based keywords in tweets over the course of a day.

Below is our first treatment of oodles of Twitter data, searching for basic patterns, happiness, and information levels. On the left, we have strong evidence that people really do tweet about what’s going on in their lives right now, at least food-wise.

The paper: Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

http://arxiv.org/abs/1101.5120

Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth

Abstract:

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators, such as gross domestic product. Here, we use a real-time, remote-sensing, non-invasive, text-based approach—a kind of hedonometer—to uncover collective dynamical patterns of happiness levels expressed by over 50 million users in the online, global social network Twitter. With a data set comprising nearly 2.8 billion expressions involving more than 28 billion words, we explore temporal variations in happiness, as well as information levels, over time scales of hours, days, and months. Among many observations, we find a steady global happiness level, evidence of universal weekly and daily patterns of happiness and information, and that happiness and information levels are generally uncorrelated. We also extract and analyse a collection of happiness and information trends based on keywords, showing them to be both sensible and informative, and in effect generating opinion polls without asking questions. Finally, we develop and employ a graphical method that reveals how individual words contribute to changes in average happiness between any two texts.

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