汉语中大概没有这个句式。当然，证明没有比证明有要困难，如果你可以在古文中找到例证，请告诉我。它和英文的「one of the most」非常相像。「最……之一」和「最」在汉语中的本意发生了矛盾。汉语的「最」有「首位」的意思，且有排他性。不过，「最」也能作程度修饰，类似于「绝」「极」「尤」，例如「最习于水性」。但是这种语境下往往没有比较含义。当比较存在时，「最」就是无争议的「第一」。
不真诚。英语的「one of the most」，「one」摆在前面。汉语的「最……之一」，「之一」摆在后面，讲话的人随时可以反悔。由于汉语「最」在比较情景下表示「第一」，加不加这个「之一」会很严重地影响语句意思，如果讲话的人可以随便反悔，对沟通和交流就会很有伤害。
su 是 switch user 的简写，不是 super user 的简写。顾名思义，使用 su 命令可以切换用户。过程中 su 命令会要求目标用户的密码，来验证人的身份。最常见的用法就是利用 su 命令获取 root 权限。这样，一个系统管理员一般需要记住两个密码：自己的用户密码和 root 密码。
村子里还有一幢 800 年前宋朝时候的房子，旁支亲戚现在还住在里边。房屋很大，很高，夏天很凉快，有漂亮的天井和科学的排水设施。打开室内水道上的盖子，凉风习习，穿孔而出，让人赞叹。门口的石阶已经不大能走路，扭曲折断，还不如普通的小坡易于攀爬，形状就像是麻省理工学院的 Ray and Maria Stata Center。这石阶不是人工的现代艺术，而是大地撕裂的痕迹，真实记录了800年来脚下这片土地的每一次震动，和那棵老树一样，共同记录了文明在自然中的成长和飘摇。
平时大家在文档排版、印刷排版的时候，不管是应用级的 Adobe inDesign 和 Microsoft Word，还是底层排版引擎 Latex，都会默认在英文字符与汉字之间、阿拉伯数字与汉字之间加一个间隙或是半角空格。不信的话大家可以打开 Word 试一下。而且，如果我们在 Word 的中英文间隔处手动打一个空格，这个间隙不变，我实验过多次。
我今天注意到，腾讯微信的排版引擎没有做这件事情。
中英混排加空格这个排版习惯，当代年轻的传统出版界人士并不了解，倒是 IT 工程师们比较清楚。现在传统媒体都直接用现成的排版软件，这些排版上的事软件都自动做了，导致传统媒体运营者并不知道其中还有这一道关窍。倒是 IT 工程师经常需要自己写原生的朴素文本文档和 HTML 代码，需要不停地手打空格，中英混排加空格这件事情已经深入脑髓。我们的 Windows 操作系统，Mac OS 操作系统，IOS 操作系统，Android 操作系统，但凡涉及中英混排的，都会遵循这个空格习惯。
[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。
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.
@inproceedings{41200, title = {All the news that’s fit to read: a study of social annotations for news reading}, author = {Chinmay Kulkarni and Ed H. Chi}, year = 2013, URL = {http://dl.acm.org/citation.cfm?id=2481334}, booktitle = {In Proc. of CHI2013}, pages = {2407-2416} }
