社交共享是评判新闻的好标准吗？

    社交共享是由公司集中控制的。浏览量是一个用以衡量开放网络，可以通过各种手段进行跟踪的量化标准。而社交共享则是由社交网络自身集中衡量，并向外发布的。社交网络可能存在宽松地计算共享数据以显得自身更受欢迎的动机，尽管出版商可以核实这些共享数据是否真正带来了页面流量。这当然是一个问题，它从属于一个更大的忧虑：许多人担心互联网本身是不是正在变得更加集中化。

    社交共享是一个大混杂。一个与上述批评意见相关的问题是，什么数据可以纳入社交共享指标，可能会因为社交网络自身的不同想法而瞬息变化。计算社交共享时往往出现一些我们可能并不认同的主观判断，比如把一条链接微博的转发也算作一次社交共享。当然，确定浏览量或者谷歌分析（Google Analytics）和Omniture提供的独立访问量服务时，同样也存在大量的主观判断。所以说，这种意见并不是什么新鲜事。但由于每家社交网络皆是其社交共享数据的独家提供商，这种忧虑的确出现了增大的趋势。

    自动分享增加了社交共享数值。有些Twitter账户的创建目的就是为了发布一些给定的RSS订阅源，所以有些文章不管质量如何，总会在Twitter上获得不少分享次数。这种情况肯定将扭曲社交共享数值，但由于Twitter留言是公开的（不计算来自私人Twitter账户的留言），这种情况一旦出现，是可以被检测到的。

    我们已经把所有的反对意见都摆放在台面上了，接下来让我们总结一下社交共享的优点：

    社交共享是一种有意图的用户行为。一个误导性标题、一张预览照片或一个搜索结果常常会很容易地怂恿我们浏览一篇我们其实不想阅读的文章。如此以来，我们就为出版商的浏览量和收入做出了贡献。但正如Buzzfeed公司负责产品事务的副总裁克里斯•约翰内森所言：“你无法哄骗某个人向他或她的朋友分享一篇文章。”在社交媒体分享一篇文章，是一种意图非常明显的用户行为，需要比一次浏览量更多的点击次数。

    社交共享关乎用户的社交网络形象。与浏览量和评论不同，人们非常用心地分享一篇文章，意味着他们愿意在Twitter上告诉整个世界或者至少告诉Facebook上的朋友，他们喜欢这篇文章。

    社交共享不依赖抽样。在如今的网络上，来自尼尔森公司（Nielsen）和康姆斯科公司（comScore）的抽样数据是少数几个相对客观的关注度衡量指标之一。做得好时，采样可以成为一个强大的指标。但正如许多宣称选举不公道的人士所知道的那样，这种做法并不完美。少数派和新平台经常未被充分代表。由于缺乏数据，采样数据只能显示某个出版物层级，而不是一篇给定文章的受关注度。与之相比，社交共享数据能够充分显示网络上每篇文章的分享次数。

    社交共享次数是公开的，因此不易造假。不同于其他任何网络衡量标准，任何人都可以看到社交共享次数。众所周知的是，许多出版商和企业家都在操纵他们向公众发布的指标。他们往往能够逃脱惩罚，因为只有他们拥有分析系统的访问权限。而社交共享则不在出版商的掌控范围，它是一个统一的尺度。

    Social shares are centrally controlled by corporations. While pageviews are a measure of the open web that can be tracked through a variety of means, social shares are centrally measured and reported by the social networks themselves. There may be an incentive for the networks to count these numbers liberally to appear more popular, though publishers can check if it's actually resulting in traffic. This is certainly a concern, and a subset of a larger worry many have about the web being more centralized in general.

    The definition of a social share is influx. Related to the prior criticism, what goes into the social share metric can change at the whim of a social network. There are judgment calls that go into calculating what a social share is, such as counting retweets of a tweet with a link as a share itself, we might not agree with. Of course, there are plenty of judgement calls that go into defining a pageview or unique visitor by services like Google Analytics (GOOG) or Omniture (ADBE), so this is nothing new. But the concern is heightened since each social network is the exclusive provider of its own share counts.

    Autosharing increases social share counts. There are some Twitter accounts set up to tweet anything in a given RSS feed, so some articles will get a handful of tweets no matter what. When this happens it definitely skews the numbers, but since tweets are public (tweets from private Twitter accounts are not counted) it can be detected when this occurs.

    Now that we've got the objections on the table, let's look at the virtues of social shares:

    Social shares are an intentional action by users. It's easy to get pulled into viewing an article you don't really want to read by a misleading headline, preview photo or search result, which results in a pageview and often revenue for the publisher. But as Buzzfeed's VP Product Chris Johanesen wrote "you can't trick someone into sharing a story with their friends." Sharing an article on social media is a very intentional act by a user, requiring several more clicks than a pageview.

    Social shares require users to put their identities on the line. Unlike pageviews and comments, when people care to share an article they're willing to tell the world on Twitter or at least their friends on Facebook (FB) that they care about the article.

    Social share counts don't rely on sampling. One of the few objective measures of attention on the web today is sampled data from the likes of Nielsen (NLSN) and comScore (SCOR). When done well, sampling can be a powerful indicator, but as many people who've miscalled elections know it's not perfect. Often minorities and new platforms are underrepresented. Sampled data can only be reported at a publication level rather than for a given article due to lack of data. Social share numbers report a full count for every article on the web.

    Social share counts are public and therefore not easy to fib about. Unlike just about every other web metric, anyone can look up social share counts. Many publishers and entrepreneurs are known to manipulate the metrics they release to the public. They've been able to get away it with since only they have access to the analytics system. Social shares are out of the control of the publishers and a uniform metric.