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by Evaristo Caraballo

通过Evaristo Caraballo

根据我对3.5GB聊天记录的分析，Emoji开发人员使用最多 (The Emoji developers use most — based on my analysis of 3.5GB of chat logs)

Emoji have drastically changed the way we communicate in social media.

表情符号已彻底改变了我们在社交媒体中进行交流的方式。

There are numerous studies suggesting differences in the way people use emoji on different social media platforms. For example, the lists of the top emoji in Instagram, Twitter, or Facebook have some similarities but also very distinctive patterns. Those differences get larger when moving down the list.

有大量研究表明，人们在不同社交媒体平台上使用表情符号的方式有所不同。例如， Instagram ， Twitter或Facebook中的顶级表情符号列表具有一些相似之处，但也具有非常独特的模式。向下移动列表时，这些差异会更大。

The possibility that the social platform dynamics might affect the use of emoji made me curious about how people might use them in a social platform to learn to code.

社交平台动态可能会影响表情符号的使用，这使我很好奇人们在社交平台上如何使用它们来学习编码。

In this article, I look at how new developers use emoji, specifically in the freeCodeCamp’s Gitter Main Chat Room.

在本文中，我将研究新开发人员如何使用表情符号，特别是在freeCodeCamp的Gitter主聊天室中。

There are at least two ways to render emoji in Gitter:

至少有两种方法可以在Gitter中渲染表情符号：

Using aliases (like those listed by existing online cheat sheets).
使用别名 (例如现有在线备忘单中列出的别名 )。
Using the UTF-8 form by either writing the emoji directly from your keyword or copying/pasting the character from online resources.
通过直接从关键字写表情符号或从在线资源复制/粘贴字符来使用UTF-8格式 。

Both render differently in the message, the former rendering existing Gitter images and the latter rendering according to your machine setups. The first method “using aliases” is the most popular and will be the main subject of this discussion.

两者在消息中的呈现方式都不同，前者呈现现有的Gitter图像，而后者则根据您的计算机设置进行呈现。第一种使用别名的方法是最流行的方法，它将成为本次讨论的主题。

To give you a quick idea of what I was after, I wanted to quickly explore answers to questions like:

为了让您快速了解自己的工作经历，我想快速探索以下问题的答案：

Is there a distinctive pattern in the use of emoji?
表情符号的使用是否有与众不同的模式？
Which are the most popular emoji then?
那么，哪些是最受欢迎的表情符号？
How many people use emoji?
有多少人使用表情符号？
How versed are users in the emoji vocabulary?
使用者对表情符号词汇的了解程度如何？

So lets get started and answer these questions.

因此，让我们开始并回答这些问题。

让我们来谈谈表情符号 (Let's have some emoji-talk)

After carrying out my analysis, I found out that about 23% of engaged chatters were also emoji users. I define an engaged chatter as a person that has sent at least 10 messages. If we instead compare engaged and non-engaged emoji users against all engaged chatters, that figure rises to 45%.

经过分析，我发现约23％的活跃聊天者也是emoji表情用户。我将参与聊天的人定义为已发送至少10条消息的人。如果我们将参与的表情符号用户和未参与的表情符号用户与所有参与的聊天者进行比较，则该数字上升到45％。

The number of emoji users might sound small compared to other platforms. However, it is important to note that:

与其他平台相比，表情符号用户的数量听起来可能很少。但是，重要的是要注意：

many users of the chat room were short lived
聊天室的许多用户都是短暂的
there were users who preferred a conservative communication
有些用户喜欢保守的交流
some users might not know the emoji aliases
一些用户可能不知道表情符号别名

In total, our emoji users rendered at least 753,000 emoji (600,000 when emoji were counted only once per message) with an average of 32 emoji for every 100 messages.

总共，我们的表情符号用户至少渲染了753,000个表情符号(每条消息仅计算一次表情符号时为600,000)，平均每100条消息32个表情符号。

All in all, our emoji users showed a collective literacy of about 800 aliases, about 25% of the full list of emoji in use. I sketched a beeswarm visualization? on D3.js showing that many of them were introduced for the first time in the chat room between July 2015 and July 2016 with a growth rate of 10 - 20 new emoji per week.

总而言之，我们的表情符号用户显示出大约800个别名的集体识字能力，约占所使用表情符号总列表的 25％。我画出了一个温暖的可视化草图？在D3.js上显示，其中许多是2015年7月至2016年7月之间在聊天室中首次引入的，每周增长10-20个新的表情符号。

When taken per individual though, our emoji users managed a vocabulary of around 3 different emoji on an average. The difference was due to few users championing the usage of emoji, with one particular emoji master showing an emoji literacy of around 500 different ones. ?

但是，当按个人使用时，我们的表情符号用户平均管理的词汇量约为3种。造成这种差异的原因是，很少有用户拥护表情符号的使用，其中一位特定的表情符号大师显示出大约500种不同的表情符号素养。？

聊天室中的“非典型”表情符号？ (“Atypical” emoji-ing in the chatroom?)

To have a better idea of how people emoji-ed in the chatroom I compared my findings against a report made by SwiftKey in 2015. There have been substantial updates to the emoji list since the release of the report but it appears to be the best free reference available still in use. It was not possible to find the emoji categorizations used by SwiftKey though. I used the categories and subcategories given by unicode.org as an approximation instead:

为了更好地了解人们在聊天室中使用表情符号的方式，我将我的发现与SwiftKey在2015年发布的报告进行了比较。自报告发布以来，表情符号列表进行了重大更新，但似乎是最好的免费软件参考资料仍在使用中。但是，无法找到SwiftKey使用的表情符号分类。我改用unicode.org给出的类别和子类别作为近似值：

I first evaluated the use of emoji at the category level and the results were very much as in the SwiftKey report. Most of the emoji posted in the freeCodeCamp chat room belonged to the “Smileys & People” category, which include faces, gestures, person-roles, body parts and hearts.

我首先在类别级别评估了表情符号的使用，其结果与SwiftKey报告中的非常相似。 freeCodeCamp聊天室中张贴的大多数表情符号都属于“笑脸与人”类别，其中包括脸部，手势，人的角色，身体部位和心脏。

Because comparisons based on high level categorizations are usually too shallow, I tried another comparison focusing on the 25 most used emoji ever from 2015 to 2017 using their subcategories instead. Together those 25 emoji accounted for around 15% of all the emoji posted during that period.

由于基于高级分类的比较通常太浅，因此我尝试了另一种比较，重点是2015年至2017年使用的子类别中 25种最常用的表情符号。这25个表情符号合起来占该时期发布的所有表情符号的15％左右。

The list of emoji and subcategories suggest that our emoji users might still fit well into the typical pattern of emoji users. The extensive use in the chat room of icons within the “face-positive” subcategory coincided with the use of the SwiftKey report's “happy faces”.

表情符号和子类别列表表明，我们的表情符号用户可能仍然很适合典型的表情符号用户模式。在聊天室中，“正面表情”子类别中图标的广泛使用与SwiftKey报告的“开心面Kong”的使用相吻合。

The same with the “face-negative” subcategory, much like the “sad faces” in the SwiftKey report. A bit apart was the use of “:trollface:”, which is an icon available in GitHub and it is usually associated with spam messages and sabotage, but also used as a joke in the freeCodeCamp chat room, probably in the same way as ? (“:poop:” or “:hankey:”), also listed in the 25 top-ever.

与“脸部阴性”子类别相同，与SwiftKey报告中的“悲伤面Kong”非常相似。稍有不同的是，使用了“：trollface：”，该图标在GitHub中可用，通常与垃圾邮件和破坏活动相关联，但在freeCodeCamp聊天室中也被用作笑话，可能的方式与？ (“：poop：”或“：hankey：”)，也列在前25名中。

However it is in the extensive use of positive hand gestures and in general “body” icons where this chat room might distinguish itself from other benchmarks.

但是，正是在积极使用手势和一般“身体”图标的广泛使用中，此聊天室才有可能与其他基准区分开。

The most used gesture icons in the freeCodeCamp chat room are positive, related to welcome, support, validation, and recognition of success, which are values commonly shared in the freeCodeCamp community.

freeCodeCamp聊天室中使用最多的手势图标是积极的，与成功的欢迎，支持，确认和认可有关，这是freeCodeCamp社区中普遍共享的价值观。

Another difference is the lesser use of icons like ♥️ “hearts” or ? “kisses”, suggesting that “sharing affection” was not the main goal of this chat room. With a gender demography of about 70–80% males that could prove even harder. This demographic might also explain some male-related icons in the top-ever, such as ? (“:gun:”).

另一个区别是较少使用诸如♥️“ hearts”或？之类的图标。 “亲吻”，这表明“ 令人讨厌的感情”不是此聊天室的主要目标。如果按性别进行人口统计，大约70-80％的男性可能会更加困难。此人口统计信息还可能会解释一些排行榜上与男性相关的图标，例如？ (“：枪：”)。

Even though we could spot some deviations to the general pattern, it is too soon to make a definitive conclusion. In fact it is likely that the most important deviations might be found in how people used the less-popular emoji.

尽管我们可以发现与一般模式的一些偏差，但要下定论尚为时过早。实际上，人们使用不太受欢迎的表情符号的方式可能会发现最重要的偏差。

Furthermore, it might be that the most important differences are not in terms of numbers, but meanings or how the iconography might be interpreted by the group according to its context. A good example of what I refer to is the swastika. A well known example for emoji is the eggplant. I wonder if from our 25 top-ever list ? (“:fire:”) wouldn’t have a distinctive meaning for this group, as a way to express “commitment to a task”. In any case, this is more a topic for those interested in social media communication and emoji, like in this article.

此外，最重要的区别可能不在于数字，而在于含义或小组根据上下文可以解释肖像的方式。我所指的一个很好的例子是十字记号。表情符号的一个众所周知的例子是茄子。我想知道是否从我们的前25名名单中脱颖而出？ (“：fire：”)对于此群体没有特殊的意义，它是表达“ 承诺完成任务”的一种方式。无论如何，对于本文中的社交媒体交流和表情符号感兴趣的人们来说，这更是一个主题。

最终获胜者是… (And the winner is…)

As a bonus, I scratched a D3.js visualization of the monthly Top5 emoji. Being part of the list of the-most-counted-ever doesn't mean that the emoji reached the monthly top 5 once, or vice versa. Like the Tour de France, a rider could be consistently in the sixth position for the whole competition without ever winning a day and then listed in the most counted. Similarly, a rider could win a day and then stay the last the rest of the time. This is why this list looks a bit different.

作为奖励，我刮擦了每月Top5表情符号的D3.js可视化效果。成为有史以来次数最多的列表的一部分并不意味着表情符号一次达到每月前5名，反之亦然。像环法自行车赛一样，骑手在整个比赛中一直处于第六名，而无需赢得任何一天，然后被列入计数最高的位置。同样，骑手可以赢得一天，然后在其余时间中保持最后。这就是为什么此列表看起来有些不同的原因。

So the winner of the monthly Top 5 is…

因此，每月最佳5强的获奖者是…

Frankly, I didn’t expect ? (“:smile:”) to be the most popular emoji. I thought it was ? (“:joy:”), given that Apple recently revealed it as its most popular during 2017.

坦白说，我没想到吗？ (“：smile：”)成为最受欢迎的表情符号。我以为是？ (“：joy：”)，因为苹果公司最近宣布它是2017年最受欢迎的产品。

The following 8 emoji also appeared in the freeCodeCamp casual chatroom. All about smiles :). Do you think you are an emoji-fan? Guess their aliases! (Observation: names/keywords can vary by platform…)

以下8个表情符号也出现在freeCodeCamp休闲聊天室中。所有关于微笑:)。您是否认为自己是表情符号迷？猜他们的别名！ (观察：名称/关键字可能因平台而异...)

I used Python and the Gitter API to get the messages from the freeCodeCamp main chat room. Python libraries like multiprocessing and emoji were used to transform the data. Part of the transformations also required data available online, for which I made customized scrapers also with Python libraries (requests, urllib, BeautifulSoup4). To analyze the data I used plain Python and some pandas. Explorative visualizations were made using matplotlib while the interactive ones where made in D3.js.

我使用Python和Gitter API从freeCodeCamp主聊天室获取消息。诸如多重处理和表情符号之类的Python库用于转换数据。部分转换还需要在线提供数据，为此，我还使用Python库(requests， urllib和BeautifulSoup4 )制作了自定义的抓取工具。为了分析数据，我使用了普通的Python和一些熊猫。使用matplotlib进行了探索性可视化，而使用D3.js进行了交互式可视化。

Versions of the code will be available on my GitHub repository together with a few final datasets. Regarding the raw datasets used for this project they are now available on the freeCodeCamp’s Kaggle account.

该代码的版本以及一些最终数据集将在我的GitHub存储库中提供。关于用于该项目的原始数据集，现在可以在freeCodeCamp的Kaggle帐户上找到它们。

The motivation of this project adheres to the mission of the freeCodeCamp’s Open Data Initiative. A big thanks to the people in the freeCodeCamp DataScience room and specially to mstellaluna for her comments!

这个项目的动机是遵循freeCodeCamp的开放数据倡议的使命。非常感谢freeCodeCamp DataScience会议室中的人员，尤其感谢mstellaluna的评论！

And remember, if you found the information in this article useful or you simply liked the content, don’t forget to leave some claps ? ? before you leave! Thanks and Happy Coding! ?

记住，如果您发现本文中的信息很有用，或者您只是喜欢其中的内容，别忘了鼓掌吗？？在你离开之前！谢谢，祝您编码愉快！？