Recent work done at HP Labs, the exploratory and research group for Hewlett Packard, shows what most of us suspected as being true all all along; that just because a person has a lot of followers, it doesn’t mean they have a lot of influence.
    作为惠普公司的探索和科研组织，惠普实验室最近公布了一项研究结果：一直以来，很多人提出的怀疑不无道理，那些有着众多“关注”的微博作者，并不一定就有巨大的影响力。

    In September 2009, using an algorithm they devised called the IP (Influence/Passivity) algorithm, a team of researchers from HP Labs continuously queried the Twitter Search API for 300 straight hours for all tweets containing the string of letters 'http'. Finding this string in a tweet would indicate the presence of a URL, and demonstrate that a web page was being shared or retweeted by means of a link.
    2009年9月，惠普实验室发明了一种被称为IP（Influence/Passivity，影响力/被动性）的算法，一组来自惠普实验室的研究人员将其连续运行了300小时，以便在国际著名微博网站Twitter Search API上查询所有包括 “http”字符串的贴子。每个包含“http”字符串的贴子，很可能就代表一个“URL”的存在，而每一个“URL”的出现，就表示一个Web页面正在被分享，或正以链接的方式被转发。

    In that time period, they acquired 22 million tweets with URLs present. This accounted for 1/15th of the entire activity of Twitter at the time. The URLs were checked for validity, and by revisiting the Twitter API they could determine who the user for each URL was, and in particular who their followers and followees were as well. From that information, a complete social graph was constructed from the dataset generated by the users sampled.
    在这段时间内，他们总共获得了2200万条带有“URL”的贴子，占当时推特微博(Twitter)活跃度的1/15。通过对这些“URL”的有效性检查，并通过修改Twitter ATI，他们可以得到：每个“URL”的用户是谁，即谁是发帖者，从而进一步得到，谁是关注的跟帖者，谁是关注的转发者。根据这些信息数据，一个完整的社交网络图谱，就由这些抽样用户生成的数据被构建起来。

    The research team worked on the following assumptions which are taken from their report "Influence and Passivity in Social Media":
    研究团队在为下述假设而工作，这些假设来自研究报告“社交媒体中的影响力和被动性”，其中提出了以下观点：
• A user’s influence score depends on the number of people she influences as well as their passivity.

一个用户的影响力得分，取决于他所影响的人的数量，以及这些人的被动性。
• A user’s influence score depends on how dedicated the people she influences are. Dedication is measured by the amount of attention a user pays to a given user as compared to everyone else.

 一个用户的影响力得分，取决于他所影响的人的专注程度。专注程度是指，用户对某一特定用户所付出的注意力高于其他用户。
• A user’s passivity score depends on the influence of those who she’s exposed to but not influenced by.

一个用户的被动性分数，取决于他所接触过，但未受其影响的那些人的影响力。
• A user’s passivity score depends on how much she rejects other user’s influence compared to everyone else.

一个用户的被动性分数，取决于相比于其他个人，该用户拒绝了多少其他用户的影响力。

    A whole industry has grown up around Twitter with the aim of developing various tools that enable Twitter users to increase their number of followers. But now all these efforts seem to have been in vain. An average Twitter user retweets only one in 318 URLs. It seems most users are passive information consumers, and do not forward the content to the network at any kind of rate that could be described as 'little more than partially engaged'. Consequently, having a large follower count is not a lot of use from a message propagation perspective if most of the followers are made up of these passive users.
    如今，整个行业已经围绕着微博成长起来。很多人的目的是，开发出各种不同的工具，使微博用户能够增加其“关注者”的数量。但是现在看来，所有这些努力似乎都白费了。平均一个微博用户只转发1/318个链接。看上去，大多数用户只是被动的信息消费者，并不向网络中转发任何内容，这种情况被描述为“稍多于部分活跃者”。因此，从信息传播角度看，如果大多数“关注者”只是这些被动用户的话，那么即使有大量“关注者”，也不能代表什么。