如果我们任由世界被虚假信息充斥，那就是在制造灾难。
谎言比真相更有力
作者：
Gray Matter
译者：邓小雪 & 黄倩霞
校对：高浦铭
策划：倪婷 & 鲁迅
How Lies Spread Online
网络谣言是如何传播的
本文选自
The New York Times
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The spread of misinformation on social media is an alarming phenomenon that scientists have yet to fully understand. While the data show that false claims are increasing online, most studies have analyzed only small samples or the spread of individual fake stories.
社交网络上的虚假信息泛滥，这一现象令人担忧，然而科学家们对此尚未有全面的认识。同时，尽管假信息在网上越来越多，但大多数研究都只分析了小范围的样本量或单个谣言的传播过程。
My colleagues Soroush Vosoughi, Deb Roy and I set out to change that. We recently analyzed the
diffusion
of all of the major true and false stories that spread on Twitter from its inception in 2006 to 2017. Our data included approximately 126,000 Twitter "cascades" (unbroken chains of retweets with a common, singular origin) involving stories spread by three million people more than four and a half million times.
我和同事Soroush Vosoughi、Deb Roy想要改变这个现状。最近，我们分析了推特自2006年成立以来至2017年的所有制造了一定影响的真实与虚假新闻是如何
传播
的。我们的数据包含了12.6万个推特"转发链"（即一连串不间断的推文转发，并且只有一个共同、单一的信息源），这些"推文链"总共涉及了300万名推特用户和超过450万次转发量。
diffuse
/ dɪˈfjuːz; dɪˋfjuz/ v [Tn] spread (sth) all around; send out in all directions 散布; 传播
Disturbingly, we found that false stories spread significantly more than did true ones. Our findings were published on Thursday in the journal Science.
令人不安的是，我们发现假新闻的传播面比真实信息更广。我们将这一发现发表在周四的《科学》杂志上。
We started by identifying thousands of true and false stories, using information from six independent fact-checking organizations, including Snopes, PolitiFact and Factcheck.org. These organizations exhibited considerable agreement - between 95 percent and 98 percent - on the truth or falsity of these stories.
首先，我们通过6家独立的信息鉴别机构（包括Snopes, PolitiFact 和Factcheck.org等）确认了上千条新闻的真假。这些机构对于新闻真实性的判断有着惊人的一致（到达了95%到98%的程度）。
Then we searched Twitter for mentions of these stories, followed the sharing activity to the "origin" tweets (the first mention of a story on Twitter) and traced all the retweet cascades from every origin tweet. We then analyzed how they spread online.
接下来，我们在推特上搜索提到这些新闻的推文，顺着"分享"追溯到原始推文（即首次提到该新闻的推特），并且从每一个原始推文追踪所有的转发链。然后我们分析了这些信息是如何在网上传播的。
For all categories of information - politics, entertainment, business and so on - we found that false stories spread significantly farther, faster and more broadly than did true ones. Falsehoods were 70 percent more likely to be retweeted, even when controlling for the age of the original tweeter's account, its activity level, the number of its followers and followees, and whether Twitter had verified the account as
genuine
. These effects were more pronounced for false political stories than for any other type of false news.
对于信息的所有类型--政治、娱乐、商业等等--我们发现相比于真实新闻来说，假新闻的传播更远、更快、更广。即便对原始推特账户的年龄、活跃度、粉丝量、关注量、账户的
真伪
是否被验证等变量进行控制，假新闻被转发的可能性仍比真实新闻高出70%，其中政治类假新闻的转发量要高于其他类型的假新闻。
genuine
/ ˈdʒenjuɪn; ˋdʒɛnjʊɪn/ adj real; truly what it is said to be; not fake or artificial 真的; 名副其实的; 非伪造的; 非人工的:
Surprisingly, Twitter users who spread false stories had, on average, significantly fewer followers, followed significantly fewer people, were significantly less active on Twitter, were verified as genuine by Twitter significantly less often and had been on Twitter for significantly less time than were Twitter users who spread true stories. Falsehood diffused farther and faster despite these seeming shortcomings.
出乎意料的是，传播假新闻的用户平均来说拥有更少的粉丝、关注更少的人、活跃度较低、认证程度较低，且比传播真实新闻的推特用户花在推特的时间上更少。但是，即便在此种情况下，假新闻还是散布地更快更广。
And despite concerns about the role of web robots in spreading false stories, we found that human behavior contributed more to the differential spread of truth and falsity than bots did. Using established bot-detection algorithms, we found that bots accelerated the spread of true stories at approximately the same rate as they accelerated the spread of false stories, implying that false stories spread more than true ones as a result of human activity.
尽管机器人账户散播虚假信息，引发人们担忧，我们发现不论新闻真假人类行为对信息传播的影响更大。借助目前的机器人账户侦查算法，我们发现这些账户对真新闻和假新闻传播速度的影响是相同的，也就是说，假新闻盖过真相是人类活动的结果。
Why would that be? One explanation is novelty. Perhaps the novelty of false stories attracts human attention and encourages sharing, conveying status on sharers who seem more "in the know."
为什么会这样呢？一种解释是假新闻更新奇。也许是这种新奇吸引了人们的注意，促使他们转发，显得自己对这个新奇事物比别人"了解更多"。
Our analysis seemed to bear out this hypothesis. Using accepted computerized methods for inferring emotional content from word use, we found that false stories inspired replies on Twitter expressing greater surprise than did true stories. The truth, on the other hand, inspired more joy and trust. Such emotions may
shed light on
what inspires people to share false stories.
我们的研究证实了这一猜想。借助目前已得到认可的，能够分析文字运用背后蕴含情感的电脑系统，我们分析了推特上假新闻和真新闻底下的评论，发现假新闻更易令人惊讶。而真相更易令人快乐和信任。这也许
说明了
是什么促使人们转发假新闻。
shed
/ ʃed; ʃɛd/ v (-dd-; pt, pp shed) [Tn, Tn.pr] ~ sth (on sb/sth) spread or send sth out 散发出（某物）
As we learn more about how and why false news spreads, we should test interventions to dampen its diffusion. For example, though it was disheartening to learn that humans are more responsible for the spread of false stories than previously thought, this finding also implies that behavioral interventions may succeed in stemming the tide of falsity. It could be, for example, that labeling news stories, in much the same way we label food, could change the way people consume and share it.
对假新闻如何传播和什么引起了假新闻的传播这两个问题有了更多了解之后，就能测试各项干预措施对抑制其传播的作用。比方说，
在假新闻上，人为因素所造成的影响比我们之前设想的要大，但研究还发现行为干预或许能成功遏制假新闻的大面积扩散。这种干预可以是标记新闻内容，就像标记食品一样，以此改变人们对内容的消费和分享。
Financial incentives are another possible tool. The social media advertising market creates incentives for the spread of false stories because their wider diffusion makes them profitable. If platforms were to demote accounts or posts that disseminated false stories, using algorithms to weed out falsehoods, the financial incentives would presumably be reduced. The tricky question, of course, would be: Who gets to decide what is true and false?
金钱激励是另一种工具。社交媒体是一个广告市场，假新闻的传播能够带来广泛的关注，从而创造收益。这些收益又刺激了假新闻的传播。如果社交平台要把传播假新闻的账户或者帖子通过算法清除掉，那金钱激励也会受影响下降。棘手的问题是，真和假由谁来判断呢？
Our research is just the beginning. A more robust identification of the factors that drive the spread of true and false news will require direct interaction with users through interviews, surveys and lab experiments. We could also benefit from randomized controlled trials of efforts to dampen the spread of false stories.
