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by Evaristo Caraballo

通過Evaristo Caraballo

根據我對3.5GB聊天記錄的分析，Emoji開發人員使用最多 (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.

表情符號已徹底改變了我們在社交媒體中進行交流的方式。

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，人們在不同社交媒體平臺上使用表情符號的方式有所不同。例如， Instagram ， Twitter或Facebook中的頂級表情符號列表具有一些相似之處，但也具有非常獨特的模式。向下移動列表時，這些差異會更大。

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.

社交平臺動態可能會影響表情符號的使用，這使我很好奇人們在社交平臺上如何使用它們來學習編碼。

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

在本文中，我將研究新開發人員如何使用表情符號，特別是在freeCodeCamp的Gitter主聊天室中。

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

至少有兩種方法可以在Gitter中渲染表情符號：

Using aliases (like those listed by existing online cheat sheets).
使用別名 (例如現有在線備忘單中列出的別名 )。
Using the UTF-8 form by either writing the emoji directly from your keyword or copying/pasting the character from online resources.
通過直接從關鍵字寫表情符號或從在線資源復制/粘貼字符來使用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.

兩者在消息中的呈現方式都不同，前者呈現現有的Gitter圖像，而后者則根據您的計算機設置進行呈現。第一種使用別名的方法是最流行的方法，它將成為本次討論的主題。

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

為了讓您快速了解自己的工作經歷，我想快速探索以下問題的答案：

Is there a distinctive pattern in the use of emoji?
表情符號的使用是否有與眾不同的模式？
Which are the most popular emoji then?
那么，哪些是最受歡迎的表情符號？
How many people use emoji?
有多少人使用表情符號？
How versed are users in the emoji vocabulary?
使用者對表情符號詞匯的了解程度如何？

So lets get started and answer these questions.

因此，讓我們開始并回答這些問題。

讓我們來談談表情符號 (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%.

經過分析，我發現約23％的活躍聊天者也是emoji表情用戶。我將參與聊天的人定義為已發送至少10條消息的人。如果我們將參與的表情符號用戶和未參與的表情符號用戶與所有參與的聊天者進行比較，則該數字上升到45％。

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

與其他平臺相比，表情符號用戶的數量聽起來可能很少。但是，重要的是要注意：

many users of the chat room were short lived
聊天室的許多用戶都是短暫的
there were users who preferred a conservative communication
有些用戶喜歡保守的交流
some users might not know the emoji aliases
一些用戶可能不知道表情符號別名

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.

總共，我們的表情符號用戶至少渲染了753,000個表情符號(每條消息僅計算一次表情符號時為600,000)，平均每100條消息32個表情符號。

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.

總而言之，我們的表情符號用戶顯示出大約800個別名的集體識字能力，約占所使用表情符號總列表的 25％。我畫出了一個溫暖的可視化草圖？在D3.js上顯示，其中許多是2015年7月至2016年7月之間在聊天室中首次引入的，每周增長10-20個新的表情符號。

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，當按個人使用時，我們的表情符號用戶平均管理的詞匯量約為3種。造成這種差異的原因是，很少有用戶擁護表情符號的使用，其中一位特定的表情符號大師顯示出大約500種不同的表情符號素養。？

聊天室中的“非典型”表情符號？ (“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:

為了更好地了解人們在聊天室中使用表情符號的方式，我將我的發現與SwiftKey在2015年發布的報告進行了比較。自報告發布以來，表情符號列表進行了重大更新，但似乎是最好的免費軟件參考資料仍在使用中。但是，無法找到SwiftKey使用的表情符號分類。我改用unicode.org給出的類別和子類別作為近似值：

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.

我首先在類別級別評估了表情符號的使用，其結果與SwiftKey報告中的非常相似。 freeCodeCamp聊天室中張貼的大多數表情符號都屬于“笑臉與人”類別，其中包括臉部，手勢，人的角色，身體部位和心臟。

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.

由于基于高級分類的比較通常太淺，因此我嘗試了另一種比較，重點是2015年至2017年使用的子類別中 25種最常用的表情符號。這25個表情符號合起來占該時期發布的所有表情符號的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”.

表情符號和子類別列表表明，我們的表情符號用戶可能仍然很適合典型的表情符號用戶模式。在聊天室中，“正面表情”子類別中圖標的廣泛使用與SwiftKey報告的“開心面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.

與“臉部陰性”子類別相同，與SwiftKey報告中的“悲傷面Kong”非常相似。稍有不同的是，使用了“：trollface：”，該圖標在GitHub中可用，通常與垃圾郵件和破壞活動相關聯，但在freeCodeCamp聊天室中也被用作笑話，可能的方式與？ (“：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，正是在積極使用手勢和一般“身體”圖標的廣泛使用中，此聊天室才有可能與其他基準區分開。

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勢圖標是積極的，與成功的歡迎，支持，確認和認可有關，這是freeCodeCamp社區中普遍共享的價值觀。

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:”).

另一個區別是較少使用諸如??“ hearts”或？之類的圖標。 “親吻”，這表明“ 令人討厭的感情”不是此聊天室的主要目標。如果按性別進行人口統計，大約70-80％的男性可能會更加困難。此人口統計信息還可能會解釋一些排行榜上與男性相關的圖標，例如？ (“：槍：”)。

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.

盡管我們可以發現與一般模式的一些偏差，但要下定論尚為時過早。實際上，人們使用不太受歡迎的表情符號的方式可能會發現最重要的偏差。

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，最重要的區別可能不在于數字，而在于含義或小組根據上下文可以解釋肖像的方式。我所指的一個很好的例子是十字記號。表情符號的一個眾所周知的例子是茄子。我想知道是否從我們的前25名名單中脫穎而出？ (“：fire：”)對于此群體沒有特殊的意義，它是表達“ 承諾完成任務”的一種方式。無論如何，對于本文中的社交媒體交流和表情符號感興趣的人們來說，這更是一個主題。

最終獲勝者是… (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.

作為獎勵，我刮擦了每月Top5表情符號的D3.js可視化效果。成為有史以來次數最多的列表的一部分并不意味著表情符號一次達到每月前5名，反之亦然。像環法自行車賽一樣，騎手在整個比賽中一直處于第六名，而無需贏得任何一天，然后被列入計數最高的位置。同樣，騎手可以贏得一天，然后在其余時間中保持最后。這就是為什么此列表看起來有些不同的原因。

So the winner of the monthly Top 5 is…

因此，每月最佳5強的獲獎者是…

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.

坦白說，我沒想到嗎？ (“：smile：”)成為最受歡迎的表情符號。我以為是？ (“：joy：”)，因為蘋果公司最近宣布它是2017年最受歡迎的產品。

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個表情符號也出現在freeCodeCamp休閑聊天室中。所有關于微笑:)。您是否認為自己是表情符號迷？猜他們的別名！ (觀察：名稱/關鍵字可能因平臺而異...)

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從freeCodeCamp主聊天室獲取消息。諸如多重處理和表情符號之類的Python庫用于轉換數據。部分轉換還需要在線提供數據，為此，我還使用Python庫(requests， urllib和BeautifulSoup4 )制作了自定義的抓取工具。為了分析數據，我使用了普通的Python和一些熊貓。使用matplotlib進行了探索性可視化，而使用D3.js進行了交互式可視化。

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.

該代碼的版本以及一些最終數據集將在我的GitHub存儲庫中提供。關于用于該項目的原始數據集，現在可以在freeCodeCamp的Kaggle帳戶上找到它們。

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!

這個項目的動機是遵循freeCodeCamp的開放數據倡議的使命。非常感謝freeCodeCamp DataScience會議室中的人員，尤其感謝mstellaluna的評論！

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! ?

記住，如果您發現本文中的信息很有用，或者您只是喜歡其中的內容，別忘了鼓掌嗎？？在你離開之前！謝謝，祝您編碼愉快！？