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  3. Humans are startlingly bad at detecting fraud. Even when we’re on the lookout for signs of deception, studies show, our accuracy

Humans are startlingly bad at detecting fraud. Even when we’re on the lookout for signs of deception, studies show, our accuracy

admin2018-02-17  78
问题    Humans are startlingly bad at detecting fraud. Even when we’re on the lookout for signs of deception, studies show, our accuracy is hardly better than chance. Technology has opened the door to new and more pervasive forms of fraud: Americans lose an estimated $ 50 billion a year to con artists a-round the world, according to the Financial Fraud Research Center at Stanford University. But because computers aren’t subject to the foibles of emotion and what we like to call "intuition," they can also help protect us. Here’s how leading fraud researchers, neuroscientists, psychiatrists, and computer scientists think technology can be put to work to fight fraud however it occurs—in person, online, or over the phone.
   Spam filters are supposed to block e-mail scams from ever reaching us, but criminals have learned to circumvent them by personalizing their notes with information gleaned from the Internet and by grooming victims over time.
   In response, a company called ZapFraud is turning to natural-language analytics; Instead of flagging key words, it looks for narrative patterns symptomatic of fraud. For instance, a message could contain a statement of surprise, the mention of a sum of money, and a call to action. "Those are the hallmark expressions of one particular fraud e-mail," Markus Jakobsson, the company’s founder, told me. "There’s a tremendous number of[spam]e-mails, but a small number of story lines. "
   A similar approach could help combat fraud by flagging false statements on social media. Kalina Bontcheva, a computer scientist who researches natural-language processing at the University of Sheffield, in England, is leading a project that examines streams of social data to identify rumors and esti mate their veracity by analyzing the semantics, cross-referencing information with trusted sources, identifying the point of origin and pattern of dissemination, and the like. Bontcheva is part of a research collaboration which plans to flag misleading tweets and posts and classify them by severity: speculation, controversy, misinformation, or disinformation.
Natural-language analytics can do the followings except______.
选项 A、testifying
B、highlighting
C、categorizing
D、comparing
答案A
解析 细节题。本题问“自然语言分析学能做以下的事情但除了……之外”。[A]尤指“出庭作证”不合题意。[B]“标记”；[C]“分类”；[D]“对比”。
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