一切适于阅读的新闻[1]：关于新闻阅读社群标注的研究
文章转载自 Google Research，是同名论文的概要说明。原文地址：http://googleresearch.blogspot.com/2014/10/all-news-thats-fit-to-read-study-of.html。译文之后我附了本文原文、初始论文等信息。原文发表于当地时间 2014 年 10 月 9 日。
作者：
Chinmay Kulkarni，斯坦福大学在读博士，前谷歌实习生
Ed H. Chi，Google Research 科学家
翻译：OrangeCLK orangeclk[at]orangeclk.com
新闻是大家日常信息食粮的一道主菜。和互联网上的其他活动一样，在线新闻阅读正在迅速地演变成一种社交体验。今天的互联网使用者可以看到各种各样来源的新闻推荐，报纸网站让读者可以互相分享新闻文章，餐厅点评网站会展示其他食客的推荐，目前一些社交网络也已经集成了社交新闻阅读器[2]。
[1] All the News That’s Fit to Print 是《纽约时报》老板奥克斯于 1896 年 10 月 25 日提出的口号，于 1897 年 2 月 10 日置于头版左上角，意指《纽约时报》刊印一切值得刊印的新闻。后来纽约时报又针对其网站提出了 All the News That’s Fit to Click。本文标题为 All the News That’s Fit to Read，化用了这一句式。参考来源：http://www.nytco.com/who-we-are/culture/our-history/#1910-1881-timeline http://en.wikipedia.org/wiki/The_New_York_Times http://www.businessinsider.com/2007/10/nyt-all-the-new——译者注。
[2] 例如 Facebook 推出了新闻阅读应用 Paper。——译者注。
新闻文章的推荐信息和赞许信息可以来自计算机与算法、发表与聚合内容的商业公司、朋友、甚至完全陌生的人。这些解释信息（即为什么这些文章会呈现给你，也就是我们所说的“标注”）会怎样影响用户的阅读选择？新闻传播中的在线社群标注已经无所不在，但是我们对标注的理解却惊人地少，用户会怎样响应这些标注，怎样才能把标注富有成效地推荐给用户？
《一切适于阅读的新闻：关于新闻阅读社群标注的研究》发表于 2013 年美国计算机学会人机交互专家协会的计算机系统人类因素研讨会[3]上，是2013年谷歌论文影响力榜单中的重点论文。在这篇论文中，我们发表了两项实验的结果。截至目今，大家普遍认为社交标注是一种提升用户参与度的常见简单方法。但实验显示，社交标注一点也不简单，不同的社交标注说服力有很大差别，提升用户参与度的能力也有很大差别。
[3] ACM 是美国计算机学会；SIGCHI 是人机交互专家协会，即 Special Interest Group on Computer-Human Interaction；译者这里将 Conference on Human Factors in Computing Systems 译作“计算机系统人类因素研讨会”——译者注。
当用户看到的内容未经个性化定制也不是来自于他们的社交网络时，他们会怎样使用社交标注？这是第一项实验关注的问题。一个典型的情境是用户正在浏览他们尚未登录的社交网络。我们把同样的一组新闻文章展示给参与研究的志愿者，给这些新闻标上来自陌生人、计算机程序、虚构品牌公司的社交标注。此外，我们还告诉志愿者他们的名字是否会出现在他们阅读的文章旁边，作为标注呈现给其他实验参与者（也就是“记录”或“不记录”他们的阅读行为）。
令人吃惊的是，在这个“尚未登录”的情境下，不知名商业公司和计算机的标注要显著地比陌生人的社交标注有说服力。这项实验结果揭示了标注在信息推荐方面的潜力，哪怕这些标注是由用户从不知晓的品牌和推荐算法产生的。实验也表明，在用户尚未登录的情境下，计算机和商业公司的标注很有价值。实验还显示，无论标注是哪种类型，开启“记录”功能后，用户对新闻文章的总点击量会变少。这表明被试者知道他们在社交阅读应用中会展现给其他用户的怎样形象。
第一项实验说明陌生人的标注不如计算机和品牌商有说服力，那么好友的标注效果如何呢？第二项实验以谷歌用户为被试者，考查他们在登录后的情境中对好友的标注有怎样的反应，探究个性化的赞许信息能否帮助人们发现、选择可能更有意思的内容。
可能并不令人多么吃惊，实验结果显示好友标注很有说服力，提升了用户对文章选择的满意度。有趣的是，在实验后的访谈中，我们发现，影响志愿者是否阅读文章的标注主要有三类：第一类，标注者和用户的社交亲密度超过了阈值；第二类，标注者有新闻文章相关领域的专业知识；第三类，标注给被推荐的文章提供了附加信息。这说明社会情境和个性标注共同作用，总体上提升了用户体验。
有待研究的一些问题包括：高亮标注中的专业知识是否能提升用户体验；社交亲密度的阈值是否可以通过算法确定；聚合标注（例如，“110名用户点了赞”）是否能提升用户参与度。我们希望下一步的研究能够解释使得社交推荐能够提供合理解释 为什么用户应该关注 解释标注呈现的更多微妙之处。
原文：All the News that’s Fit to Read: A Study of Social Annotations for News Reading
Posted by Chinmay Kulkarni, Stanford University Ph.D candidate and former Google Intern, and Ed H. Chi, Google Research Scientist
News is one of the most important parts of our collective information diet, and like any other activity on the Web, online news reading is fast becoming a social experience. Internet users today see recommendations for news from a variety of sources; newspaper websites allow readers to recommend news articles to each other, restaurant review sites present other diners’ recommendations, and now several social networks have integrated social news readers.
With news article recommendations and endorsements coming from a combination of computers and algorithms, companies that publish and aggregate content, friends and even complete strangers, how do these explanations (i.e. why the articles are shown to you, which we call “annotations”) affect users’ selections of what to read? Given the ubiquity of online social annotations in news dissemination, it is surprising how little is known about how users respond to these annotations, and how to offer them to users productively.
In All the News that’s Fit to Read: A Study of Social Annotations for News Reading, presented at the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems and highlighted in the list of influential Google papers from 2013, we reported on results from two experiments with voluntary participants that suggest that social annotations, which have so far been considered as a generic simple method to increase user engagement, are not simple at all; social annotations vary significantly in their degree of persuasiveness, and their ability to change user engagement.
The first experiment looked at how people use annotations when the content they see is not personalized, and the annotations are not from people in their social network, as is the case when a user is not signed into a particular social network. Participants who signed up for the study were suggested the same set of news articles via annotations from strangers, a computer agent, and a fictional branded company. Additionally, they were told whether or not other participants in the experiment would see their name displayed next to articles they read (i.e. “Recorded” or “Not Recorded”).
Surprisingly, annotations by unknown companies and computers were significantly more persuasive than those by strangers in this “signed-out” context. This result implies the potential power of suggestion offered by annotations, even when they’re conferred by brands or recommendation algorithms previously unknown to the users, and that annotations by computers and companies may be valuable in a signed-out context. Furthermore, the experiment showed that with “recording” on, the overall number of articles clicked decreased compared to participants without “recording,” regardless of the type of annotation, suggesting that subjects were cognizant of how they appear to other users in social reading apps.
If annotations by strangers is not as persuasive as those by computers or brands, as the first experiment showed, what about the effects of friend annotations? The second experiment examined the signed-in experience (with Googlers as subjects) and how they reacted to social annotations from friends, investigating whether personalized endorsements help people discover and select what might be more interesting content.
Perhaps not entirely surprising, results showed that friend annotations are persuasive and improve user satisfaction of news article selections. What’s interesting is that, in post-experiment interviews, we found that annotations influenced whether participants read articles primarily in three cases: first, when the annotator was above a threshold of social closeness; second, when the annotator had subject expertise related to the news article; and third, when the annotation provided additional context to the recommended article. This suggests that social context and personalized annotation work together to improve user experience overall.
Some questions for future research include whether or not highlighting expertise in annotations help, if the threshold for social proximity can be algorithmically determined, and if aggregating annotations (e.g. “110 people liked this”) help increases engagement. We look forward to further research that enable social recommenders to offer appropriate explanations for why users should pay attention, and reveal more nuances based on the presentation of annotations.
论文下载地址
http://pan.baidu.com/s/1o6ofZq6
论文 BibTex 代码：
1 | @inproceedings{41200, |
