Research into Fake News in the 2016 election

After reviewing the literature on the issue, several common research methods and communication theories emerged.  Prior research studies have involved conducting message system analysis, or content analysis, of Facebook posts and tweets that researchers deemed fake or deliberately misleading.  Allcott and Gentzkow (2017) conducted the first-known study on the impact of fake news in 2016, drawing on a database of 156 election-themed news stories that they concluded were fabricated.  They also conducted a 1,200-person survey following the election to try to determine whether voters recalled some of the fake stories that users were spreading online.  They concluded that because pro-Trump fake news had primarily been seen by people who were already inclined to vote for him, they would expect the falsehoods to have a small impact on voting decisions. 

In another study, Bovet and Makse (2019) examined 30 million tweets that contained a link to a news story in the 5 months before the election.  Based on an independent classification of the linked sites, they determined that 25% of the tweets contained fake or extremely biased news. 

Guess, Nagler, and Tucker (2019) linked a survey of 3,500 people in a national sample in the United States to the respondents’ history on Facebook to determine how much fake news they might have shared.  Perhaps reassuringly, they concluded that the vast majority of users on that platform did not share fake news at all in the lead-up to the 2016 election. 

Guess, Nyhan, and Reifler (2018) surveyed 2,525 Americans who consented to provide their web traffic data from their computers.  They estimated that one in four Americans visited a fake news site in the 2 months before the election day. 

Researchers at Yale University explored the concept of prior exposure to determine whether being shown fake news stories at a higher frequency would increase the perception that the made-up stories were true (Pennycook, Cannon, & Rand, 2018).  Using a sample of 1,069 participants, they conducted a three-stage experiment.  In the first stage, the familiarization stage, they showed participants six headlines that were accurate and six that were fake news using a format similar to a Facebook post.  In the second step, the distractor stage, the contributors filled out a set of demographic questions to indicate their voting preferences.  Finally, they took part in the assessment stage, where they were shown 24 news headlines (12 from the first stage and 12 new ones, six of which were fake and six that were real).  They were asked to rate each headline for accuracy and familiarity.  The researchers concluded that even a single exposure to a fake story was enough in increasing the perceived accuracy of the story.  This occurred despite some of the stories being labeled as “Disputed by Third Party Fact-Checkers” (Pennycook et al., 2018, p. 1871).  Because they saw the same fake stories repeatedly cycling through their social media feeds, the study suggests that repetition could increase perceived believability in users’ online activities.  Indeed, the study’s authors demonstrated the concept that if one repeats a lie often enough, it becomes the truth.