Among other findings, in this paper we demonstrate that human ratings of the happiness of an individual word correlate very strongly with the average happiness of the words that co-occur with it. This implies that tweets containing particular keywords can be used as an unsolicited public opinion poll.

For example, tweets containing “Tiger Woods” became decidedly less positive after his Thanksgiving disaster in 2009 as the words ‘accident’, ‘crash’, ‘scandal’, and ‘cheating’ are more abundant, while the word ‘love’ appears less often.

Happiness is measured relative to the ambient background of all tweets.

Sad words are blue, happy words are yellow. Up (down) arrow indicates that the word appeared more (less) frequently in tweets containing "Tiger Woods".

Generally, tweets containing personal pronouns tell a positive prosocial story with ‘our’ and ‘you’ outranking ‘I’ and ‘me’ in happiness. The least happy pronoun on our list is the easily demonized ‘they’.

Emoticons in increasing order of happiness are ‘:(’, ‘:-(’, ‘;-)’, ‘;)’, ‘:-)’, and ‘:)’. In terms of increasing information content (diversity of words co-occuring with each emoticon), the order is ‘:(’, ‘:-(’, ‘:)’, ‘:-)’, ‘;)’, and ‘;-)’. We see that happy emoticons co-occur with words of higher levels of both happiness and information but the ordering changes in a way that appears to reflect a richness associated with cheekiness and mischief: the two emoticons involving semi-colon winks are third and fourth in terms of happiness but first and second for information.

A list of the happiness ratings of tweets containing some interesting keywords can be seen here.

And not surprisingly, the happiness of all tweets appearing on a given day of the week correlates well with the happiness ratings humans give each day.

Happiness of tweets appearing on a given day

Human ratings of the happiness of each day of the week

You can download the language assessment by Mechanical Turk (labMT 1.0) word list here. It is a text file containing the set of 10,222 most frequently occurring words in the New York Times, Google Books, music lyrics, and tweets, as well as their average happiness evaluations according to users on Mechanical Turk.  See the paper for details.

Much more to come regarding sociotechnical phenomena…

Do you laugh within your tweets? e.g. hahaha!!!  Here we show the number of times these different laugh species appear in tweets as a function of how many ha‘s they contain.  A few observations:

1. Longer laughs are less frequent, and the frequency decays at a constant rate. We’re plotting on logarithmic axes, the black line has a slope of -5 and appears to match the data over at least 5 decades in frequency… Zipf would be proud of the people: Hahaha power law?
2. ha is less frequent than haha but slightly more frequent than hahaha.
3. Only a select few humans are able to make it out beyond 100 letters without a typo.  Congratulations!

Thanks to one of our students, Tyler Gray, for sorting this all out.

PDF versions available here. Made possible by data ninjas Kameron Harris and Morgan Frank.

Abstract:

Within the last million years, human language has emerged and evolved as a fundamental instrument of social communication and semiotic representation. People use language in part to convey emotional information, leading to the central and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languages neutrally, positively, or negatively biased? Previous findings are mixed: suggestive evidence of a positive bias has been found in small samples of English words [1-3], framed as the Pollyanna Hypothesis [3] and Linguistic Positivity Bias [1], while the experimental elicitation of emotional words has instead found a strong negative bias [4]. Here, we report that the human-perceived positivity of over 10,000 of the most frequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of word positivity for four large and distinct corpora, demonstrating that their form is surprisingly invariant with respect to frequency of word use.

Vibrating cornstarch and water in slow motion, narration by xtranormal.

A google search for ‘ooblexity’ returns “Did you mean: complexity?”  Maybe I did.

More info on the experiment, and a 10 log-decade spread in material costs:

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

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.