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This is Scientific American — 60-Second Science. I'm Christopher Intagliata.
Twitter has more than 300 million monthly active users. But researchers have estimated that between about 30 million and 50 million of those are Twitter bots—automated accounts that do the bidding of their code-writing creators.
"There could newsbots, and there could be spam bots." Zafar Gilani, a PhD student at the University of Cambridge in the U.K. "Or there could be bots doing political infiltration, which is obviously bad. Or social infiltration which could be bad."
Not all bots are bad. Some are just geeky, like a bot that describes imaginary exoplanets. Or another that tweets only prime numbers. "It really depends on who the botmaster is and what are the intentions and what are the motivations."
Gilani and his colleagues built an algorithm to single out bots from human accounts, using factors like tweet frequency or content, and how much users interacted with other users. And the system was able to tell bot from human 86 percent of the time.

But in the case of celebrity accounts—people with more than 10 million followers—the bots and humans were harder to tell apart. Because both tend to tweet with more scheduled regularity than the average human. Both follow relatively few people. And both upload a lot of content.
They differ in the details: celebrities don't post as many URLs luring people off Twitter. And they don't retweet as often as bots do either.
The researchers presented the findings at the International Conference on Advances in Social Networks Analysis and Mining in Sydney, Australia.
As bots get smarter and more pervasive online, we humans can still console ourselves with a different discovery by Gilani: that tweets penned by humans get 19 times more likes than tweets by bots. And 10 times more retweets. So, at least in Twitter popularity, humans still beat the bots...for now.
Thanks for listening for Scientific American — 60-Second Science Science. I'm Christopher Intagliata.

参考译文

这里是科学美国人——60秒科学9,n)aqx(*9t|k83xE。我是克里斯托弗·因塔利亚塔)Cv-NLAY,7pA~z#H。
推特的月度活跃用户超过3亿lt8MflNX#g4eYV。但研究人员估计，其中约3000万到5000万是推特机器人，即执行代码编写人员指令的自动账户2l0bZioYC!4;u&pgsVb。
“可能是新闻机器人，也可能是垃圾邮件机器人wNHjYU1uvyk。”在英国剑桥大学就读博士的扎法尔·吉拉尼说道^OFt^e1;(&p,p=@)XC。“或者可能是机器人在进行政治渗透，显然这很糟糕hCx.c8@D)Bm;mn#,M5;0。又或者是机器人在进行社会渗透，这也不好W=u=BqZf)&。”
不过并不是所有机器人都是不好的MQyzsdksQuQ。有一些机器人只是比较呆，比如有的机器人账号描绘虚构的系外行星X6a*b^XNif~。还有的账号只发和质数有关的消息K(6aJ!b+Q*11J=。“这其实取决于账号操控者的身份及其意图和动机j6kumFA-sKU6&。”
吉拉尼和同事创建了一种能将机器人从真人账号中区分出来的算法，这种算法依据的是发布频率或内容以及该用户与其他用户的互动程度b4B[X^og6YK8i6nn0e。该系统区分人类账号和机器人账号的准确率为86%#v[JVvNNWrbNC5GJf。
但对于拥有超过1000万粉丝的名人账号，系统则很难区分那是机器人还是人类~.912b+42l)2b8。因为二者更新的频率比普通人更规律g8wfGHSXOQ609.L5BJ。另外，二者关注的人都相对较少，而且都会上传大量内容3KvOxY%pwNF&ep。
不过二者在细节上存在区别：名人不会发太多链接诱使人们离开推特SP8cC2J@&c8mpsF。他们也不会像机器人那样频繁转发4Hsp]9[-iWHNdJ。
研究人员在澳大利亚悉尼举办的“社会网络分析和挖掘进展国际会议”上发表了这一研究结果1PB5-(mor](ChBg。
虽然机器人越来越智能，在网络上越来越普遍，但我们人类仍然能够用吉拉尼独特的发现来安慰自己：人类发布推文的点赞数是机器人的19倍，转发量则是机器人的10倍g+=Ll@*jxhE9@j。所以，至少就推特人气来说，人类依旧战胜了机器人……到目前为止是这样Ji|_,uODeJRlwTQ3c。
谢谢大家收听科学美国人——60秒科学yug~1~V![dk。我是克里斯托弗·因塔利亚塔9MSfZ,a1w[Sm+fO。

译文为可可英语翻译，未经授权请勿转载！

重点讲解

重点讲解：
1. depend on 取决(于)；有赖(于)；
例句：The efficiency of such a solar cell would depend on the materials used for the electrode, according to the researchers.
研究人员指出，以此机制为基础的太阳能电池的效率将取决于电极材料的使用uJ)|oW~sdjq0j-M5。
2. single out 单独挑出；特别选出；
例句：It would be invidious to single out any one person to thank.
单独感谢任何一个人都易引起反感Q1DCBtu~3x^]jL+6Y(。
3. interact with 相互交流；相互沟通；
例句：After initiation, the youths started to interact with the older members.
入会仪式后，这些小伙子与老成员开始互动@RFN@=q4U#v。
4. tell apart 区分；区别；辨别；
例句：Even when magnified, the two organisms are difficult to tell apart.
即使放大之后，这两种有机体也难以区分2C=h4]l8fY。