Suchergebnisse

Researchers have long measured people's thoughts, feelings, and personalities using carefully designed survey questions, which are often given to a relatively small number of volunteers. The proliferation of social media, such as Twitter and Facebook, offers alternative measurement approaches: automatic content coding at unprecedented scales and the statistical power to do open-vocabulary exploratory analysis. We describe a range of automatic and partially automatic content analysis techniques and illustrate how their use on social media generates insights into subjective well-being, health, gender differences, and personality.

The Internet has forever changed the way people access information and make decisions about their healthcare needs. Patients now share information about their health at unprecedented rates on social networking sites such as Twitter and Facebook and on medical discussion boards. In addition to explicitly shared information about health conditions through posts, patients reveal data on their inner fears and desires about health when searching for health-related keywords on search engines. Data are also generated by the use of mobile phone applications that track users' health behaviors (e.g., eating and exercise habits) as well as give medical advice. The data generated through these applications are mined and repackaged by surveillance systems developed by academics, companies, and governments alike to provide insight to patients and healthcare providers for medical decisions. Until recently, most Internet research in public health has been surveillance focused or monitoring health behaviors. Only recently have researchers used and interacted with the crowd to ask questions and collect health-related data. In the future, we expect to move from this surveillance focus to the "ideal" of Internet-based patient-level interventions where healthcare providers help patients change their health behaviors. In this article, we highlight the results of our prior research on crowd surveillance and make suggestions for the future.

When aggregating the probability estimates of many individuals to form a consensus probability estimate of an uncertain future event, it is common to combine them using a simple weighted average. Such aggregated probabilities correspond more closely to the real world if they are transformed by pushing them closer to 0 or 1. We explain the need for such transformations in terms of two distorting factors: The first factor is the compression of the probability scale at the two ends, so that random error tends to push the average probability toward 0.5. This effect does not occur for the median forecast, or, arguably, for the mean of the log odds of individual forecasts. The second factor—which affects mean, median, and mean of log odds—is the result of forecasters taking into account their individual ignorance of the total body of information available. Individual confidence in the direction of a probability judgment (high/low) thus fails to take into account the wisdom of crowds that results from combining different evidence available to different judges. We show that the same transformation function can approximately eliminate both distorting effects with different parameters for the mean and the median. And we show how, in principle, use of the median can help distinguish the two effects.

In October 2013, multiple United States (US) federal health departments and agencies posted on Twitter, "We're sorry, but we will not be tweeting or responding to @replies during the shutdown. We'll be back as soon as possible!" These "last tweets" and the millions of responses they generated revealed social media's role as a forum for sharing and discussing information rapidly.