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Pentagon Wants to Predict Anti-Trump Protests Using Social Media Surveillance

A series of research projects, patent filings, and policy changes indicate that the Pentagon wants to use social media surveillance to quell domestic insurrection and rebellion.

The social media posts of American citizens who don’t like President Donald Trump are the focus of the latest US military-funded research. The research, funded by the US Army and co-authored by a researcher based at the West Point Military Academy, is part of a wider effort by the Trump administration to consolidate the US military’s role and influence on domestic intelligence.

The vast scale of this effort is reflected in a number of government social media surveillance patents granted this year, which relate to a spy program that the Trump administration outsourced to a private company last year. Experts interviewed by Motherboard say that the Pentagon’s new technology research may have played a role in amendments this April to the Joint Chiefs of Staff homeland defense doctrine, which widen the Pentagon’s role in providing intelligence for domestic “emergencies,” including an “insurrection.”

It’s no secret that the Pentagon has funded Big Data research into how social media surveillance can help predict large-scale population behaviours, specifically the outbreak of conflict, terrorism, and civil unrest.

Much of this research focuses on foreign theatres like the Middle East and North Africa — where the 2011 Arab Spring kicked off an arc of protest that swept across the region and toppled governments.

Since then, the Pentagon has spent millions of dollars finding patterns in posts across platforms like Facebook, Twitter, Instagram, Tumblr, and beyond to enable the prediction of major events.

But the Pentagon isn’t just interested in anticipating surprises abroad. The research also appears to be intended for use in the US homeland.

Datasets for the research were collected using the Apollo Social Sensing Tool, a real-time event tracking software that collects and analyses millions of social media posts.

The tool was originally developed under the Obama administration back in 2011 by the US Army Research Laboratory and US Defense Threat Reduction Agency, in partnership with Rensselaer Polytechnic Institute, the University of Illinois, IBM, and Caterva (a social marketing company that in 2013 was folded into a subsidiary of giant US government IT contractor, CSC). Past papers associated with the project show that the tool has been largely tested in foreign theatres like Haiti, Egypt, and Syria.

But the use of the Apollo tool to focus on protests in the US homeland has occurred under the Trump administration. The ‘election’ dataset compiled using Apollo for the 2018 US Army-funded study is comprised of 2.5 million tweets sent between October 26, 2016, and December 20, 2016, using the words “Trump”, “Clinton,” and “election.”

Tweets were geolocated to focus on “locations where protests occurred following the election” based on user profiles. Locations were then triangulated against protest data from “online news outlets across the country.”

The millions of tweets were used to make sense of the “frequencies of the protests in 39 cities” using 18 different ways of measuring the “size, structure and geography” of a network, along with two ways of measuring how that network leads a social group to become “mobilized,” or take action.

In short, this means that “the social network can be a predictor of mobilization, which in turn is a predictor of the protest.” This pivotal finding means that extensive real-time monitoring of American citizens’ social media activity can be used to predict future protests.