Predicting The Future Through Social Networks

Nicholas West
Activist Post

The emergence of Big Data, predictive analytics, and artificial intelligence is leading to new ways to more accurately predict the future.

A new method of monitoring identifies what information will be relevant on social networks up to two months in advance. This may help predict social movements, consumer reactions or possible outbreaks of epidemics, according to a study in the Universidad Carlos III of Madrid (UC3M).

This particular study is using Twitter as its resource. It is interesting to see the mention of attempting to predict “possible outbreaks of epidemics” as this is exactly the solicitation that was put out by the Environmental Protection Agency (EPA) earlier this year. And this is just the tip of the iceberg for the deeper role of social media.

As Melissa Melton reported, the EPA was zeroing in on keywords to data mine for evidence of the flu, but also could have corollary uses:

there are those pesky anti-vaccine advocates, nicknamed “Flugitives,” who simply refuse to line up for their flu vaccine:

A national campaign has begun with the intention to shame and peer pressure everyone to get the flu shot. The campaign was created by Sanofi-Pasteur, the company who makes…you guessed it…a flu vaccine called Fluzone, approved by our good friends at the FDA in 2011. (They also collaborate with the notable eugenicists of the Bill and Melinda Gates Foundation.) You can find out more about the FLUgitive campaign on Facebook

There’s also the potential usefulness of combating, say, adverse reaction reports to vaccines as they happen… you know, to reassure the public that the flu shot is totally safe in case they should get any other “misinformation” about it. (Source)


The system being studied at the University of Madrid is described in the video below as one that identifies certain people as “sensors” – those people who play central roles within social networks. It is thought that these central personas give rise to the viral nature of certain content. Researchers are then looking for the friend networks that are established which would lead back to the sensor individuals who would be most likely to know and spread information before anyone else. This is the heart of what is called the “friendship paradox” – essentially that your friends will generally have more friends than you.

The stated applications have disturbing implications: “detecting demonstrations, political views, or the spread of epidemics through the messages people write.” Or, one could surmise – the spread of political epidemics, such as anything that emerges to contest the official narrative.

This video is in Spanish, but you can click the CC (captions) button in the bottom right corner of the video to read along in English.

What is further interesting to note is that it is possible to analyze in real time. So if there is any question remaining about the dual-use nature of social networks, this should lay it to rest. The press release states it in no uncertain terms:

Is it possible to find a group of people (sentinels or sensors) with a special position that would allow the information that “goes viral” globally on the internet to be monitored? “If we could do that, we would be able to predict that viral spread, which would allow us to better understand social mobilization, debates regarding opinions, health, etc., and to determine how they become global,” explains one of the researchers, Esteban Moro Egido, of the Interdisciplinary Complex Systems Group at UC3M. (emphasis added)

And once identified, the phase of social engineering can presumably be implemented.  Here is one concrete example that researchers observed: Obamacare.

“We were really surprised. We thought the method would give us a few hours early warning, but instead it gave us several days, and sometimes even weeks or months,” says co-senior author, James Fowler, professor of medical genetics and political science at the University of California-San Diego (USA). For example, the sensor model predicted the “viral” rise of the hashtag “#Obamacare” as a Twitter trend, detecting it two months before it peaked on Twitter, and three months before it reached the highest number of Google searches with that name. 

Simple and effective 

In general, this new method turns out to be very simple and effective for monitoring social networks, according to its creators. Data from just 50,000 Twitter (users) is enough to achieve these levels of prediction and to know what will “go viral” across the entire Internet. It can be used in real time, about different topics, in different languages and geographical areas, thus allowing for different contexts to be covered: discovering new opinions in a political debate, predicting social movements, obtaining previous knowledge of consumers’ reactions to new products, or analyzing how messages regarding certain illnesses or epidemics are spread in the public health arena. 

[…] 

This is undoubtedly a new way of predicting the future by analyzing the data that circulates on the social networks. (emphasis added)

This system is one more step toward creating a world that is mapped in advance by those with the power to collect and analyze data on all of us, largely without our knowledge.

We are already beginning to see the implementation of pre-crime police systems, patents for the use of predictive behavior tech to trigger product shipments, the aforementioned concern over flu vaccine evaders, and an intense interest in governmental use of social media keywords to monitor, start or disrupt political activity. The Cuban Twitter scandal might be the best example as even the Associated Press had to report that “the primitive social media network, known as ZunZuneo . . . aimed to stir political unrest in Cuba. The network was created under the U.S. Agency for International Development, but its users were unaware it was backed by the U.S. government.”

Given the trend of current applications and intentions for the use of predictive behavior technology by corporations and governments, the only question that remains is will it ever really be put to use toward something positive?

Source:
http://www.eurekalert.org/pub_releases/2014-04/ciuo-asd042814.php

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