Last night, I attended a panel discussion on “Algorithms and Automation in Journalism” hosted 通过 my alma mater, the 梅里尔新闻学院 at the University of Maryland. My fellow alum 亚当·奥斯丘, chief strategy officer at Mashable, moderated the discussion, which included Nick Diakopolous, UMD assistant professor, and Mashable’s 梅根种社会, assistant real-time news editor.

亚当和梅根提供了一个镜头，让我们看到他们在Mashable做新闻决策时，是如何平衡算法的力量和人类判断的需要的。 以下是要点：

• 关于 four years ago, Mashable developed its own predictive analytics tool called Velocity to forecast which news stories will trend, but they also rely on third-party tools, like Dataminr (an “initial signal”) and Storyful (“for user-generated content and to verify stories”)
• 一些广告商和广告代理商正在使用Velocity进行实时的品牌内容创作和分发
• “Data frees us to do real 新闻,” said Specia. “Making the phone calls, doing the research … access to data takes out the time-consuming act of filtering all the noise.”
• Mashable gets 55% of its traffic from social channels, but those who come directly to the site are offered top picks, which are selected 通过 people (“prescriptive”) while algorithms drive the others
• Diakopolous suggested third-party services could do more to allow news organizations to plug in the characteristics they care about in order to yield richer data and ensure a “diverse media ecology”
• Perhaps the most intriguing part of the conversation, which Diakopolous called “the question of our time,” is whether news organizations should be more transparent about how/when/where they’re using algorithms for editorial judgment. 数据是从哪里来的，它是否干净，你会披露后来发现的数据中的错误吗？

Diakopolous建议新闻媒体至少应该写一篇博客来解释他们是如何使用数据的。 CBS周日早间节目的活动嘉宾杰伊·克尼斯（Jay Kernis）想知道，算法的使用是否会导致一种新型的司法特派员来回答观众关于导致特定故事的数据的问题。

算法在你每天的新闻推送中扮演什么角色？ Would knowing which content is 100% automated change your media diet?