😱Real Sources / Fake News

After finding evidence that much of the “fake” and hyper-biased news traffic during 🇺🇸#Election2016 was arriving through direct hyperlinks, search engines, and “old school” sharing tactics such as email newsletters, RSS, and instant messaging, I thought I would do a small “big data” project.

I wrote this piece because I feel the argument about Facebook’s role in influencing the outcome of the U.S. election doesn’t address the real problem: the sources of the fake/misleading/hyper-biased information. Sure, Google’s ad network and Facebook’s News Feed/“Related Stories” algorithms amplify the emotional spread of misinformation, and social media naturally turn up the volume of political outrage. At the same time, I think journalists, researchers and data geeks should first look into the factors that are actually 1) producing the content and 2) driving the online traffic.

Rather than analyze “known unknowns” with incomplete metrics and partial analytics (i.e., measuring the famously opaque Facebook engagement metrics), this analysis looks directly at the source.

⚗Welcome to the Micro-Propaganda Machine

There’s a vast network of dubious “news” sites. Most are simple in design, and many appear to be made from the same web templates. These sites have created an ecosystem of real-time propaganda: they include viral hoax engines that can instantly shape public opinion through mass “reaction” to serious political topics and news events. This network is triggered on-demand to spread false, hyper-biased, and politically-loaded information.

For this analysis, I’m calling it “fake news.”

It’s what I term the #MPM: the “micro-propaganda machine” — an influence network that can tailor people’s opinions, emotional reactions, and create “viral” sharing (😆LOL/haha/😡RAGE) episodes around what should be serious or contemplative issues. The increasing influence of this type of behavioral micro-targeting and emotional manipulation — data-driven “psyops” — has become more noticable as people begin to reflect on the outcome of the recent #Brexit and U.S. election.

In my previous post, I found that only ~60% of incoming traffic from a sample of leading “fake” and hyper-biased news sites seemed to be coming out of Facebook and Twitter. The remaining ~40% of web traffic was organic — coming from direct website visits, P2P shares, text/instant messaging, subscription e-newsletters, RSS, and search engines. Again: Less than 0.1% of the traffic to the sites I looked at came from display advertising or (known) paid search content.