Examining the Tech Stacks of Czech and Slovak Untrustworthy Websites

Though commentators frequently warn about “echo chambers,” little is known about the volume or slant of political misinformation people consume online, the effects of social media and fact-checking on exposure, or its effects on behavior. We evaluate these questions for the websites publishing factually dubious content often described as “fake news.” Survey and web traffic data from the 2016 U.S. presidential campaign show that Trump supporters were most likely to visit these websites, which often spread via Facebook. However, these sites made up a small share of people’s information diets on average and were largely consumed by a subset of Americans with strong preferences for pro-attitudinal information. These results suggest that widespread speculation about the prevalence of exposure to untrustworthy websites has been overstated.

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.