双语阅读｜社交媒体上的假新闻传得比真相快
ACROSS the French countryside, in the summer of 1789, rumours swirled about vengeful aristocrats bent on the destruction of peasants’ property. It was not true. The Great Fear, as it is now known, tipped France into revolution with a flurry of fact-free gossip and rumour.
1789年的夏天，法国农村谣传心怀报复的贵族决想要毁掉农民的农田——引发了“法国大恐慌”事件。然而，事实并非如此。但正如今天人们所熟知的，“大恐慌”以一阵没有事实根据的流言蜚语和谣言之风使法国陷入革命。
Two centuries later the methods for spreading nonsense are much improved. In the first paper of its kind, published in Science on March 8th, Soroush Vosoughi and his colleagues at the Massachusetts Institute of Technology present evidence that, on Twitter at least, false stories travel faster and farther than true ones.
200年后，流言蜚语的传播方式改进很多。3月8日，第一篇有关传播谣言方式的研究论文在《科学》期刊上发表情 麻省理工学院的苏鲁什·沃梭基及其同事在文中给出证据——至少在推特上，虚假新闻远比真相传播的速度快得多。
The study, carried out at MIT’s Laboratory for Social Machines, showed this by examining every tweet sent between 2006 and 2017. The researchers used statistical models to classify tweets as false or true, by applying data taken from six independent fact-checking organisations. That allowed them to categorise over 4.5m tweets about 126,000 different stories. Those stories were then ranked according to how they spread among Twitter’s users.
此项研究是由麻省理工学院社会机器实验室进行的，调查2006年至2017年期间的所有推文并得出这个结论。研究人员利用从六个独立信息核实机构得来的数据，使用统计模型对推文进行虚假和真实分类。这个统计模型可以对450多万推文中的约126,000种新闻报道加以类别。根据这些新闻在推特用户中的传播方式进行排列。
The results were stark. False information was retweeted by more people than the true stuff, and faster to boot. True stories took, on average, six times longer than falsehoods to reach at least 1,500 people. Only about 0.1% of true stories were shared by more than 1,000 people, but 1% of false stories managed between 1,000 and 100,000 shares.
研究结果非常明显。与真实信息相比，更多人会转发虚假消息，且传播的速度更快。要让1,500人获得真相所需要的时间平均是虚假新闻传播时间的6倍。1,000多人中分享的信息里仅约0.1%是真实的，而1,000至100,000人之间分享的信息1%是虚假的。
The reason false information does better than the true stuff is simple, say the researchers. Things spread through social networks because they are appealing, not because they are true. One way to make news appealing is to make it novel. Sure enough, when the researchers checked how novel a tweet was (by comparing it, statistically, with other tweets) they found false tweets were significantly more novel than the true ones. Untrue stories were also more likely to inspire emotions such as fear, disgust and surprise, whereas genuine ones provoked anticipation, sadness, joy and trust, leading to the rather depressing conclusion that people prefer to share stories that generate strong negative reactions. Perhaps not coincidentally, fake political news was the most likely to go viral.
研究人员称，虚假消息比真相传播快的原因很简单。通过社交网络传播的东西，并不是因为它们真实，而是因为更具吸引力。要让这些新闻消息变得有吸引力的一个方式就是使其变得新奇。果不其然，在查验一条推文有多新奇（统计时，与其他推文进行对比）时，研究人员发现虚假信息明显比真实信息更新奇。虚假新闻也更可能激起读者恐惧、反感和惊喜等情感，而真相激发的是人们的期待感、悲伤感、喜感和信任感，结果结论颇令人忧愁——人们更喜欢分享能令人产生强烈消极反应的消息。或许虚假政治新闻最可能快速传播并非巧合。
The paper also sheds some of the first peer-reviewed light on the impact of “bots”—automated accounts posing as real people. The idea that Russian bots in particular helped sway America’s presidential election has lodged itself firmly in the public consciousness. Yet the paper finds that, on Twitter at least, the presence of bots does not seem to boost the spread of falsehoods relative to truth.
论文还阐述了一些同行评议中首先提到的“僵尸”程序——假冒真人的自动账户——的影响。特别是俄罗斯僵尸程序影响了美国总统选举这一想法牢牢地印在公众意识之中。然而，论文指出，至少在推特上，僵尸程序似乎并没有拉大虚假新闻的传播与真相相关比例。
The researchers were able to conduct a study of this breadth thanks to the business relationship between one of their number, Deb Roy, and Twitter, which provided its entire historical dataset at a steep discount. But more are likely to come. Technology companies, and particularly social-media firms, are facing a backlash from regulators and consumers worried about the harm from their products. Twitter, for its part, has said it is ready to offer the same dataset to other outside experts.
研究人员能够对虚假新闻传播进行如此具有广度的研究，得亏德布·罗伊和推特之间的业务关系，才能以这么低的价格获得这么完整的历史数据库。各科技公司，尤其是社交媒体公司都要面对来自监管机构和用户的强烈抵制，监督机构和用户担心科技公司的产品会给人带来伤害。对于推特，也表示准备好给外界专家提供同样的数据库。
编译：彭展峰
编辑：翻吧君
来源：经济学人（2018.03.10）