outrage in another ten or twenty years, or will we have grown used to it? More to the point, will Facebook still bother to publish their findings, or will they simply run experiments for their own private benefit? What is troubling about the situation today is that the power inequalities on which such forms of knowledge depend have become largely invisible or taken for granted. The fact that they combine âbenignâ intentions (to improve our health and well-being) with those of profit and elite political strategy is central to how they function. The only way in which such blanket administration of our everyday lives can now be challenged is if we also challenge the automatic right of experts to deliver any form of emotion to us, be it positive or negative.
The truth of happiness?
How happy were you yesterday? How did you feel? Do you know? Can you remember? Itâs possible that, even if you donât, someone else could tell you. As the digital and neurological sciences of happiness progress, they are nearing the point where experts are more qualified to speak about your subjective state than you are. Or to put that another way, subjective states are no longer subjective matters.
Twitter is a case in point. Twitterâs 250 million users produce 500 million tweets per day, producing a constant stream of data which can potentially be analysed for various purposes. This is one of the more dramatic examples of big data accumulation in recent years. Ten per cent of this stream is made freely available at no cost, opening up enticing opportunities for social researchers, both in business and universities. The rest of the stream, up to the complete fire-hose of every single tweet, is available for a range of fees.
The research challenge is how to make sense of so much data, which involves building algorithms capable of interpreting millions of tweets. At the University of Pittsburgh, a group of psychologists has built one such algorithm, aimed at capturing how much happiness is expressed in a single 140-character tweet. To do this, the researchers created a database of five thousand words, drawn from digital texts, and gave every word a âhappiness valueâ on a scale of 1â9. A tweet can then be automatically scored in terms of its expression of happiness.
The Pittsburgh project is designed to spot trends in happiness at an aggregate level, analysing 50 million tweets every day. It is not in itself interested in the happiness levels of individual users. Instead, it can identify some clear patterns in how happinessfluctuates across the population, both over time and over space. Happiness maps have been developed on the back of this data; the researchers now know that Tuesday is the least happy day of the week, and Saturday the most happy. This project might not actually report back on how happy you were last week. But a range of similar projects could, typically under the auspices that it would be for your own well-being, health or safety.
One such project is the âDurkheim Projectâ, developed by researchers at Dartmouth College, named after Ãmile Durkheim. Durkheim is known as one of the founders of sociology, and author of Suicide , an analysis of variations in national suicide rates in the nineteenth century. Durkheim was drawing on the new statistical data on death rates that had recently accrued over a number of decades in Europe at the time. The Durkheim Project aimed to go one better: drawing on analysis of social media data and mobile phone conversations, suicide would be predicted.
The targets of this analysis are former US military veterans, who are known to have a higher risk of suicide than the rest of the population. The question is how to identify those who need help before it is too late. With support from the Department of Veterans Affairs, who are able to access medical records as an additional source of data, the Durkheim Project aims to provide an early warning system
The truth of happiness?
How happy were you yesterday? How did you feel? Do you know? Can you remember? Itâs possible that, even if you donât, someone else could tell you. As the digital and neurological sciences of happiness progress, they are nearing the point where experts are more qualified to speak about your subjective state than you are. Or to put that another way, subjective states are no longer subjective matters.
Twitter is a case in point. Twitterâs 250 million users produce 500 million tweets per day, producing a constant stream of data which can potentially be analysed for various purposes. This is one of the more dramatic examples of big data accumulation in recent years. Ten per cent of this stream is made freely available at no cost, opening up enticing opportunities for social researchers, both in business and universities. The rest of the stream, up to the complete fire-hose of every single tweet, is available for a range of fees.
The research challenge is how to make sense of so much data, which involves building algorithms capable of interpreting millions of tweets. At the University of Pittsburgh, a group of psychologists has built one such algorithm, aimed at capturing how much happiness is expressed in a single 140-character tweet. To do this, the researchers created a database of five thousand words, drawn from digital texts, and gave every word a âhappiness valueâ on a scale of 1â9. A tweet can then be automatically scored in terms of its expression of happiness.
The Pittsburgh project is designed to spot trends in happiness at an aggregate level, analysing 50 million tweets every day. It is not in itself interested in the happiness levels of individual users. Instead, it can identify some clear patterns in how happinessfluctuates across the population, both over time and over space. Happiness maps have been developed on the back of this data; the researchers now know that Tuesday is the least happy day of the week, and Saturday the most happy. This project might not actually report back on how happy you were last week. But a range of similar projects could, typically under the auspices that it would be for your own well-being, health or safety.
One such project is the âDurkheim Projectâ, developed by researchers at Dartmouth College, named after Ãmile Durkheim. Durkheim is known as one of the founders of sociology, and author of Suicide , an analysis of variations in national suicide rates in the nineteenth century. Durkheim was drawing on the new statistical data on death rates that had recently accrued over a number of decades in Europe at the time. The Durkheim Project aimed to go one better: drawing on analysis of social media data and mobile phone conversations, suicide would be predicted.
The targets of this analysis are former US military veterans, who are known to have a higher risk of suicide than the rest of the population. The question is how to identify those who need help before it is too late. With support from the Department of Veterans Affairs, who are able to access medical records as an additional source of data, the Durkheim Project aims to provide an early warning system
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