Assessing the Effect of 'Disputed' Warnings and Source Salience on Perceptions of Fake News Accuracy

Pennycook, Gordon; Bear, Adam; Collins, Evan; Rand, David G.

doi:10.2139/ssrn.3035384

Cited by 60 publications

(25 citation statements)

References 12 publications

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“…Indeed, Pennycook et al found that participants were generally skeptical of fake news stories, and were quite effective at distinguishing real from fake news. Moreover, Pennycook et al found that giving participants an additional reason to be skeptical of fake news (in the form of an explicit warning) did decrease later perceptions of fake news accuracy (see also Clayton et al, 2019;Pennycook, Bear, Collins, & Rand, 2017)-it just did not interact with the boosting effect of familiarity. Thus, it appears that general skepticism may play an important role in determining who falls for fake news, despite the apparent fact that familiarity increases perceptions of accuracy.…”

Section: The Psychology Of Fake Newsmentioning

confidence: 99%

“…The headlines were presented in the format of a Facebook post-namely, with a picture accompanied by a headline, byline, and a source (e.g., "countercurrentnews.com"). Given that much of the public's engagement with news on social media involves only reading article headlines (Gabielkov, Ramachandran, & Chaintreau, 2016), we follow other recent studies on fake news (Bronstein et al, 2018;Clayton et al, 2019;Pennycook, Bear et al, 2017;Pennycook et al, 2018) and focus on judgments about news headlines and not full articles. Bullshit receptivity was measured using the scale developed by Pennycook et al (2015), overclaiming was measured using a shortened version of the questionnaire developed by Paulhus et al (2003), and analytic thinking was measured using the CRT originally developed by Frederick (2005).…”

Section: Current Workmentioning

confidence: 99%

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Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking

Pennycook

Rand

2019

Journal of Personality

Self Cite

484

410

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Objective Fake news represents a particularly egregious and direct avenue by which inaccurate beliefs have been propagated via social media. We investigate the psychological profile of individuals who fall prey to fake news. Method We recruited 1,606 participants from Amazon’s Mechanical Turk for three online surveys. Results The tendency to ascribe profundity to randomly generated sentences—pseudo‐profound bullshit receptivity—correlates positively with perceptions of fake news accuracy, and negatively with the ability to differentiate between fake and real news (media truth discernment). Relatedly, individuals who overclaim their level of knowledge also judge fake news to be more accurate. We also extend previous research indicating that analytic thinking correlates negatively with perceived accuracy by showing that this relationship is not moderated by the presence/absence of the headline’s source (which has no effect on accuracy), or by familiarity with the headlines (which correlates positively with perceived accuracy of fake and real news). Conclusion Our results suggest that belief in fake news may be driven, to some extent, by a general tendency to be overly accepting of weak claims. This tendency, which we refer to as reflexive open‐mindedness, may be partly responsible for the prevalence of epistemically suspect beliefs writ large.

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Section: The Psychology Of Fake Newsmentioning

confidence: 99%

Section: Current Workmentioning

confidence: 99%

Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking

Pennycook

Rand

2019

Journal of Personality

Self Cite

484

410

View full text Add to dashboard Cite

show abstract

“…These effects were later documented by several studies [9,15,23,25,27,46,50,56]. Social media have been proven as effective tools to influence individuals' opinions and behaviors [4-6, 13, 14] and some studies even evaluated the current tools to combat misinformation [42]. Computational tools, like troll accounts and social bots, have been designed to perform such type of influence operations at scale, by cloning or emulating the activity of human users while operating at much higher pace (e.g., automatically producing content following a scripted agenda) [26,34,38,54] -however, it should be noted that bots have been also used, in some instances, for positive interventions [40,47].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

Badawy¹,

Ferrara²,

Lerman³

2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

243

199

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Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of, among other things, using trolls (malicious accounts created for the purpose of manipulation) and bots (automated accounts) to spread misinformation and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million elections-related posts shared on Twitter between September 16 and October 21, 2016 by about 5.7 million distinct users. This dataset included accounts associated with the identified Russian trolls. We use label propagation to infer the ideology of all users based on the news sources they shared. This method enables us to classify a large number of users as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls about 31 times more often than liberals and produced 36 times more tweets. Additionally, most retweets of troll content originated from two Southern states: Tennessee and Texas. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users were exposed to Russian Trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message.

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“…Many articles that have been rated as false by major fact-checking organizations have not been flagged in Facebook's system, and two major fake news sites have seen little or no decline in Facebook engagements since early 2016 (Funke 2018). Facebook's now-discontinued strategy of flagging inaccurate stories as "Disputed" can modestly lower the perceived accuracy of flagged headlines (Blair et al 2017), though some research suggests that the presence of warnings can cause untagged false stories to be seen as more accurate (Pennycook and Rand 2017). Media commentators have argued that efforts to fight misinformation through fact-checking are "not working" (Levin 2017) and that misinformation overall is "becoming unstoppable" (Ghosh and Scott 2018).…”

Section: Introductionmentioning

confidence: 99%

Trends in the Diffusion of Misinformation on Social Media

2019

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We measure trends in the diffusion of misinformation on Facebook and Twitter between January 2015 and July 2018. We focus on stories from 570 sites that have been identified as producers of false stories. Interactions with these sites on both Facebook and Twitter rose steadily through the end of 2016. Interactions then fell sharply on Facebook while they continued to rise on Twitter, with the ratio of Facebook engagements to Twitter shares falling by approximately 60 percent. We see no similar pattern for other news, business, or culture sites, where interactions have been relatively stable over time and have followed similar trends on the two platforms both before and after the election. *

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Assessing the Effect of 'Disputed' Warnings and Source Salience on Perceptions of Fake News Accuracy

Cited by 60 publications

References 12 publications

Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking

Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking

Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

Trends in the Diffusion of Misinformation on Social Media

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