Fighting misinformation on social media using crowdsourced judgments of news source quality
Fighting misinformation on social media using crowdsourced judgments of news source quality
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DOI：
10.1073/pnas.1806781116
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发表时间：
2019-02-12
影响因子：
11.1
通讯作者：
Rand, David G.
中科院分区：
文献类型：
--
作者：
Pennycook, Gordon;Rand, David G.
减少错误信息的传播，特别是在社交媒体上，是一项重大挑战。我们研究了一种潜在的方法：让社交媒体平台算法优先显示来自用户认为值得信赖的新闻来源的内容。要做到这一点，我们问众包的信任评级是否可以有效地区分更多和更不可靠的来源。我们进行了两个预先注册的实验（来自Mechanical Turk的n = 1，010，来自Lucid的n = 970），其中个人对来自三个类别的60个新闻来源的熟悉度和信任度进行了评估：（i）主流媒体，（ii）超党派网站，（iii）生产公然虚假内容的网站（“假新闻”）。尽管存在很大的党派差异，但我们发现，政治领域的外行人认为主流来源比超党派或假新闻来源更值得信赖。虽然民主党人的这种差异比共和党人大-主要是由于民主党人对主流来源的不信任-但在两项研究中，当民主党人和共和党人的评分相等时，每一个主流来源（有一个例外）都被评为比每一个超党派或假新闻来源更值得信赖。此外，政治上平衡的外行评级与专业事实检查员提供的评级密切相关（r = 0.90）。我们还发现，特别是在自由主义者中，认知反思能力较高的人能够更好地区分低质量和高质量的来源。最后，我们发现，排除那些不熟悉给定新闻来源的参与者的评级大大降低了人群的有效性。我们的研究结果表明，让算法对来自可信媒体的内容进行排名，可能是打击社交媒体上错误信息传播的一种有前途的方法。
Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n = 1,010 from Mechanical Turk and n = 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content ("fake news"). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans-mostly due to distrust of mainstream sources by Republicans-every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r = 0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low-and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.