谣言
新颖性
假新闻
集合(抽象数据类型)
社会化媒体
欺骗
政治
计算机科学
互联网隐私
心理学
政治学
社会心理学
法学
程序设计语言
作者
Soroush Vosoughi,Deb Roy,Sinan Aral
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2018-03-08
卷期号:359 (6380): 1146-1151
被引量:7284
标识
DOI:10.1126/science.aap9559
摘要
We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.
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