The Sonic fandom has achieved a level of notoriety that few fandoms on the Internet enjoy. The art is known for being distorted, disturbing and in many cases, explicit. In my latest Youtube video, I scraped DeviantArt to analyze fan art to determine whether or not it truly lives up to this reputation. This post will walk you through exactly how I did that.

Sonic狂热者已经达到了互联网上很少有狂热者享有的臭名昭著的水平。 该技术以失真，令人不安并且在许多情况下是明确的而闻名。 在我最新的Youtube视频中 ，我刮过DeviantArt来分析粉丝艺术，以确定它是否真正符合这一声誉。 这篇文章将引导您完全了解我的操作方式。

I first wanted to get a sense of how many Sonic artworks there are on DeviantArt, a fan art sharing website. No scraping required, here — I simply searched for “Sonic” on DeviantArt and recorded how many results came up. I also did the same for similar characters, like Shrek and Pikachu. Here are those results, visualized:

我首先想了解一下粉丝艺术分享网站DeviantArt上有多少Sonic艺术品。 无需刮擦，这里-我只是在DeviantArt上搜索“ Sonic”并记录了多少结果。 我对类似史莱克和皮卡丘的角色也做过同样的事情。 这些是可视化的结果：

The amount of fan art dedicated to Sonic dwarfs that of the other characters, coming to around 1.4 million. Wow! Now that we’ve seen there’s a thriving Sonic culture on DeviantArt, it’s time to move towards answering our research question: Is Sonic fan art really that disturbing?

专门用于Sonic的粉丝数量比其他角色少了140万。 哇！ 现在我们已经看到DeviantArt上蓬勃发展的Sonic文化，是时候回答我们的研究问题了：Sonic粉丝艺术真的那么令人不安吗？

To start, I used BeautifulSoup to scrape the posts (the full code for this can be accessed here on Github). The following code scrapes the first 2200 links that came up when searching “sonic.”

首先，我使用BeautifulSoup抓取了帖子(有关完整的代码，可以在Github上访问)。 以下代码抓取了搜索“音速”时出现的前2200个链接。

base_url = "https://www.deviantart.com/search/deviations?"urls = np.array([])for i in range(50):    if i == 0:        url = base_url + "q=sonic"    else:        url = base_url + "page=" + str(i) + "&q=sonic"    request=urllib.request.Request(url,None,headers)    if url in urls:        pass    else:        bs = BeautifulSoup(urlopen(request), "html.parser")        links = [item.get("href") for item in bs.find_all(attrs={"data-hook" : "deviation_link"})]        urls = np.append(urls, links)len(urls)

I then retrieved several attributes from each of these urls, including the post’s title, tags and the number of views, favorites and comments each post got. Here is the code for retrieving this data:

然后，我从每个这些URL中检索了几个属性，包括帖子的标题，标签以及每个帖子获得的视图数，收藏夹和评论。 这是用于检索此数据的代码：

for i in range(num):    print(deviationurls[i])    request=urllib.request.Request(deviationurls[i],None,headers)     bs = BeautifulSoup(urlopen(request), "html.parser")    vals = [item.text for item in bs.find_all("span", class_="iiglI")]    tag = [item.text for item in bs.find_all("span", class_="_3uQxz")]    #print(vals)    if len(vals) == 3:        faves.append(vals[0])        comments.append(vals[1])        views.append(vals[2])    else:        faves.append(vals[0])        comments.append(0)        views.append(vals[1])    tags.append(tag)    titles.append(bs.find_all("h1")[0].text)

Sometimes, the comments field was empty, hence the if/else condition.

有时，注释字段为空，因此为if / else条件。

After doing this, you can now construct a dataframe of Sonic fan artworks for analysis! Yours might look different from mine because different art works might turn up for your query when scraping. But here is the resulting dataset from my scraper.

完成此操作后，您现在可以构建Sonic粉丝艺术品的数据框进行分析！ 您的作品可能与我的看起来有所不同，因为在抓取时可能会出现不同的艺术品供您查询。 但是这是我的刮板生成的数据集。

Here’s the distribution of years in which the artworks in my dataset were published. You can see that artworks from the 2010s represents the majority — perhaps people started posting more in this time, or perhaps those pieces are simply what came up first when I was scraping.

这是我的数据集中的艺术品发表的年份分布。 您可以看到2010年代的艺术品占了大多数-也许这时候人们开始发表更多作品，或者这些作品只是我刮scrap时首先出现的作品。

I can’t include any of the images from the dataset in this Medium post, because I do not have the right to republish them here. I do, however, react to the top 10 most viewed and most favorited pieces of fan art in my video.

我无法在此Medium帖子中包含来自数据集的任何图像，因为我无权在此处重新发布它们。 不过，我的确对我的视频中观看次数最多，最受欢迎的十大粉丝艺术作品做出了回应。

In my dataset, the two most viewed artworks were character makers (number one, and number two). This is not surprising, as a huge part of the Sonic fandom is to make original characters, or “OCs,” and write stories around these characters, like in any fandom. (The Sonic fandom, however, takes this pastime to a new level. Try googling your name + “the hedgehog” sometime to see what I’m talking about)

在我的数据集中，观看次数最多的两个艺术品是角色制作者( 第一和第二 )。 这并不奇怪，因为Sonic狂热者的绝大部分是制作原始角色或“ OC”，并像在任何狂热者中一样围绕这些角色写故事。 (但是，Sonic狂热分子将这种消遣带入了一个新的高度。尝试用谷歌搜索您的名字+“刺猬”，看看我在说什么)

Animated shorts, comics and character references were also popular.

动画短裤 ， 漫画和角色 参考也很受欢迎。

My ultimate goal was to analyze the tags that artists on DeviantArt use. Which tags are used with which? To do this, I took the “tags” column in my dataframe to create a correlation matrix. This was a somewhat involved process — the first step was to create a “corpus” of the tags for parsing.

我的最终目标是分析DeviantArt上的艺术家使用的标签。 哪些标签与哪个标签一起使用？ 为此，我在数据框中使用“标签”列来创建相关矩阵。 这是一个有点复杂的过程-第一步是创建标签的“语料库”以进行解析。

data = np.unique(df.tags)[1:](data[0])data_real = []for strdata in data:    str1 = strdata.replace(']','').replace('[','')    l = str1.replace("'",'').split(",")    l = [item.strip(' ') for item in l]    data_real.append(l)texts = [[word.lower() for word in line] for line in data_real]corpus = []for text in texts: corpus.append(‘ ‘.join(text))corpus

After creating the corpus, I used scikit-learn to create the correlation matrix.

创建语料库后，我使用scikit-learn创建了相关矩阵。

from sklearn.feature_extraction.text import CountVectorizercv = CountVectorizer(ngram_range=(1,1), stop_words = 'english') # You can define your own parametersX = cv.fit_transform(corpus)Xc = (X.T * X) # This is the matrix manipulation stepXc.setdiag(0)names = cv.get_feature_names() # This are the entity names (i.e. keywords)df_tags = pd.DataFrame(data = Xc.toarray(), columns = names, index = names)df_tags

And your resulting dataframe should look something like this (yes, it will be massive). Here, the number represents the amount of time both tags were used with one another.

您得到的数据框应该看起来像这样(是的，这将是巨大的)。 在此，数字表示两个标签相互使用的时间。

I first used Python libraries like networkx and matplotlib to visualize the network, but these were indiscernable messes. It was clear I needed a more powerful software.

我首先使用诸如networkx和matplotlib之类的Python库来可视化网络，但是这些都是混乱不堪的。 很明显，我需要一个功能更强大的软件。

I saved the dataframe to a CSV and inputted it into gephi. In the network, the larger a word is, the more times it’s used. And the closer a word is to another word, the more times those tags are used together. After fine tuning the network’s features, this is what it ended up looking like:

我将数据框保存为CSV并将其输入到gephi中。 在网络中，单词越大，使用的次数就越多。 一个词与另一个词越接近，这些标签一起使用的次数就越多。 在对网络功能进行了微调之后，最终结果如下所示：

This network is obviously massive, but it’s interesting to look around—I suggest taking a few minutes to zoom in and around it to see the connections between certain words. Or, you can watch my YouTube video summarizing this analysis and see what I took out of the network.

这个网络显然是巨大的，但是环顾四周很有趣-我建议花几分钟进行放大和环绕，以查看某些单词之间的联系。 或者，您可以观看我的YouTube视频，总结该分析并了解我从网络中获取的内容。

And that’s the end! To conclude, while there are examples of disturbing or distorted Sonic fan art, I think my analysis shows that that is certainly not what’s most popular in the community, or even the majority of the work. Much of it is, in fact, quite wholesome.

到此为止！ 总而言之，虽然有一些令人困扰或扭曲的Sonic粉丝艺术的例子，但我认为我的分析表明，这肯定不是社区中最受欢迎的，甚至不是大部分作品。 实际上，其中大部分是非常有益的。

If you think this article leaves much to be desired, please consider watching my video on the subject — I share more of my results, findings and key takeaways there. This post was more meant to explain how I did the analysis.

如果您认为这篇文章还有很多不足之处，请考虑观看我关于该主题的视频 -我在那分享了我的更多结果，发现和主要收获。 这篇文章旨在说明我是如何进行分析的。