New social media algorithm can predict the future

Rest assured, governments, and corporations will use this.

Social media can be used to predict the future. This conclusion has long been suspected, and now there is another study to confirm it. The problem is the predictive power could then be used to control people.

LOS ANGELES, CA (California Network) -- New research shows that social media can be used to predict the future. Analysis of social media, including Facebook, Twitter, and Instagram allows experts to predict civil unrest, disease outbreaks, and even rain. These predictions enjoyed 80 percent accuracy and will only be refined as more data is gathered. Even crime can be predicted in advance. However, stocks and sports scores were virtually impossible to predict.

The findings were published in a paper on Arxiv. The paper was about a system called EMBERS (Early Model Based Event Recognition using Surrogates).

Civil unrest in Brazil was predicted with 80 percent accuracy. In Venezuela, the model did not work so well, it predicted civil unrest only 50 percent of the time.

The model was able to predict when diseases would spread, such as norovirus. It was also used to predict the weather.

Of 25 types of crime examined by the researchers, 19 were somewhat predictable.

Mental health diagnoses could also be performed. Researchers found that depression was accurately diagnosed, also with 80 percent accuracy.

Researchers learned that certain hashtags were used by people. The frequency of their use and the intensity of the traffic also provided critical clues.

The researchers will not be getting rich soon. The model was useless in predicting sports scores and the stock market.

The findings would be an interesting footnote if not for the wide range of abuses that could result. Corporate interests are already seeking the technology for their own use. If businesses are better able to predict consumer behavior, they can position themselves to profit, or at least minimize losses.

Researchers think they can improve the model with more data and more time.