Twitter works for journalists as an alert system and becomes an interesting source in cases where the news develops so fast that the media are not able to cover it. But is there any wisdom in the Twitter cloud? Published in Memeburn: http://memeburn.com/2010/05/searching-for-%ef%bb%bfwisdom-in-the-twitter-cloud/
Last week together with a colleague, I was ‘ashed in’ at Amsterdam airport; he could not fly to Johannesburg and my flight to Cyprus was cancelled, all due to the ash cloud from that famous Iceland volcano Eyjafjallajokull. Twitter proved to be a good help; following the tweets gave us an idea who was flying to where or not; what is the ash cloud doing, moving to where, what is weather report, favorable or not. Ah you can’t get home, let’s share a ride, or take the train together. On Twitter we could easily find some answers.
Invisible hand
Journalists who followed the tweets got a good picture about what was going. Searching in the piles of tweets, brought up interesting links to sources from explaining the background of this phenomenon, to the amount of losses of the companies and the number of people involved. We were surfing the crowd in order to discover some truth. Is there a collective wisdom in the twitter cloud? Although I like Adams Smith ‘s simile about the ‘invisible hand’ which creates order in the market, the twitter cloud is more like a huge pub were everybody is talking and shouting while the music is playing loud. In some corners interesting things are happening more or less spontaneous and uncoordinated. Think for example about Wikipedia or open source software like Linux.
Last year I tried to analyze the tweets about an airplane crash of Turkish Airlines at Schiphol airport near Amsterdam and compared that collection of tweets with several CoverItLive sessions about the same event also using Twitter as input. The collection of tweets did not show any clear pattern of topics or consensus about what happened. The CoverItLive session on the contrary showed after some time consensus about number of death, number of people in the plane etc. So I concluded that surfing the crowd, trying to establish some truth, is difficult if there is not a minimum sense of community and direct interaction between the Twitterati. Other research showed that the social network of followers and followees does not reveal a structure, but underneath it is a structured interactive network of friends (persons who were sending direct message). See for example: Social networks that matter: Twitter under the microscope by Huberman, Romero and Fang Wu. (http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2317/2063)
Buttocks
Perhaps I was expecting too much. Twitter is not about knowledge; it is a thermometer deep into the buttocks of society; it is about the mood; popular hash tags tell us what is cool and what is hot. But if you know the mood about a certain topic, perhaps you can predict an outcome on basis of the number of tweets. This is the idea behind the study of Sitaram Asur and Bernardo Huberman, Predicting the Future with Social Media. (http://arxiv.org/abs/1003.5699) . The researchers, both working for Hewlett-Packard, analyzed in the beginning of 2009 2.89 mill tweets about 24 movies send by 1.2 mill users. A statistical analysis learned that there was a relationship between the number of tweets in the week before the movie was released and the first weekend Box-office revenues. For the horror movie The Crazies for example the statistical model predicted a revenue of 16.8 mill. The Box-office revenue was 16.06 mill. Compared to the Hollywood Stock Exchange, where people can bet on success or failure of a movie, the model performed significantly better.
After the first weekend people had actually seen the movie, and of course they twittered: they were positive or negative. Including the content of the tweets improved the model for prediction. For the second weekend the number of tweets and their content showed that positive responses improved the sales and negative responses decreased the revenue.
Herbert Blankensteijn, a Dutch journalist and blogger for one of the national newspapers, wondered if you can apply this model to the stock exchange to predict the rise and fall of the stock. Asur and Huberman had no answer. And for the moment I don’t want to bet my money on that. The application of the model for marketing is obvious. With Twitter a company can try to predict the sales of their product. It also proves that there is some wisdom in the Twitter cloud.
