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研究发现 更新推特的可能是机器人

标签:机器人 可能 系 账号 二者  日期:2017-12-19 18:34
推特拥有超过3亿的月度活跃用户,不过并不是所有机器人都是不好的,有一些机器人只是比较呆,还有的账号只发和质数有关的消息,这种算法依据的是发布频率或内容,系统则很难区分是机

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. 推特拥有超过3亿的月度活跃用户。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。 "There could newsbots, and there could be spam bots," said Zafar Gilani, a PhD student at the University of Cambridge in the U.K. 在英国剑桥大学就读博士的扎法尔·吉拉尼说道:“可能是新闻机器人,也可能是垃圾邮件机器人。” "Or there could be bots doing political infiltration, which is obviously bad. Or social infiltration which could be bad." “或者可能是机器人在进行政治渗透,这显然很糟糕。又或者是机器人在进行社会渗透,这也不好。” Not all bots are bad. Some are just geeky, like a bot that describes imaginary exoplanets. Or another that tweets only prime numbers. 不过并不是所有机器人都是不好的。有一些机器人只是比较呆,比如有的机器人账号会描绘虚构的系外行星。还有的账号只发和质数有关的消息。 "It really depends on who the botmaster is and what are the intentions and what are the motivations." “这其实取决于账号操控者的身份及其意图和动机。”

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. 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。该系统区分人类账号和机器人账号的准确率为86%。 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. 但对于拥有超过1000万粉丝的名人账号,系统则很难区分是机器人还是人类。因为二者更新的频率要比普通人更规律。另外,二者关注的人都相对较少,而且都会上传大量内容。 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. 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。他们也不会像机器人那样频繁转发。 The researchers presented the findings at the International Conference on Advances in Social Networks Analysis and Mining in Sydney, Australia. 研究人员是在于澳大利亚悉尼举办的“社会网络分析和挖掘进展国际会议”上发表的这一研究结果。

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