This time it's all from the very end, but not all from the very end.
And the eye of Jesus.
Yes.
That's a good story.
Yes.
This is actually...
Can you explain?
Can I have a mic, please?
Yes.
Does it look like this?
Okay.
Is everything okay?
Yes.
I can hear myself speaking, so it should be okay for you as well.
First of all, thank you for coming for this late afternoon session.
And the sun is shining if you want to just run out.
You have a few seconds.
I will be talking about social network sites that is dedicated to artworks.
And how we are trying to analyze this network using different techniques and tools,
and how we are trying to combine these differences.
So, in general, I can say for this specific study, for this paper,
our aim is to discuss the conceptual and technical tools
to define innovative artistic content in such a closed user community,
and to seek quantitative measures for the specification of this content.
So, I usually ask when I talk about DeviantArt,
it's the name of the site I'm talking about,
I ask the audience if they're already familiar with the site.
Anyone heard it?
Okay.
It's actually better than my art historical audience.
Usually people really look at me in the eye,
and if I'm talking about some bizarre new thing that comes from the feature.
So, DeviantArt has been around actually quite a long while.
It has been launched in 2000.
It's one of the oldest SNS that is specifically dedicated to artworks,
and usually it always comes in the first top 10 most visited websites in the category of art.
And it's ever growing, of course, as other SNS do.
So, the last time I have checked, like a few weeks ago,
it had more than 19 million members with a huge archive of around 2,200 images, million.
What is really important for us or for me as an art historian
is that it also has a very, very nicely organized category structure.
And I will come to that why it's important.
So, of course, we didn't crawl the whole website.
So, we don't have the whole 19 million member and all the information about all of them.
But the good thing is that we are in touch with DeviantArt headquarters,
so we received our data directly from them.
So, we have clean data, but the bad thing is we cannot share the data.
So, that's our agreement.
We have asked for different case studies,
and for this study, the data that we are using comes from daily deviations.
So, what are daily deviations?
In the jargon of the website, every member is called a deviant.
So, every artwork that is uploaded is a deviation.
And the DeviantArt headquarters has a long tradition in this.
They are promoting artworks that they like, or in general, actually, the community likes.
How they are choosing those artworks is the topic of another paper.
I cannot go in there, but they have quite an intricate way of doing it.
So, they choose for every day 20 to 30 different artworks,
which they published on the home webpage for the duration of a whole day.
So, you are very famous that day.
You receive a lot of hits. People see your artwork.
And the nice thing about this dataset is that the way they are choosing the artworks,
they are trying to be really balanced.
So, they really don't want to break the hearts of the new members, for example,
by, of course, the older members will have a bigger follower group.
So, it would be normally more popular and more liked.
So, they are trying to keep a balance.
Sometimes you see among those chosen artworks, like old ones from seven years ago,
or sometimes just new published ones.
So, it's a good mixture and it gives, it prepares a good overview of the structure of DeviantArt.
You see the numbers, so I'm not going into that. That's kind of the boring part.
Also, I should just give you an idea about what kind of information we could,
we can get out of the website.
So, each member has its own homepage.
And each member is free to give some personal information,
like geographic location or age or gender or your name, whatever you want.
Beside that, there are some site statistics that are published by DeviantArt
that the members cannot turn off.
So, those site statistics, such as how many people have left comments,
how many people have visited your webpage,
how many people are following you,
following is, in the Deviant terminology, is watching.
So, if someone is watching a member, then all the notification
will be sent about the actions of that member, which is really useful.
If you just put a new artwork, all your watchers will know and visit your page,
leave comments, so you'll get a lot of new hits.
Beside this homepage statistics, at the homepage,
you also have access to the gallery of each member.
Gallery is kind of self-explanatory.
Every member's artwork, everything that is uploaded is put under the gallery.
There is a second gallery that is called Favorites Gallery,
which is actually kind of a mini-curation of each member.
You just browse other members' websites, galleries,
and the works that you like, it's really easy.
You hit just a button, I like this, and then it becomes part of your own gallery.
It's a very nice way to browse DeviantArt itself, actually,
instead of using the search functionality, which in DeviantArt doesn't function.
The next thing that is available for us is that every artwork has its own page.
For every artwork, we also have statistics.
How many people have downloaded it? How many people have seen it?
How many people have commented, liked it, etc.
In theory, also in practice, we have lots of information,
lots of different ways of analyzing this network.
It's just not one network. It just depends on the question.
You should filter out some data, decide what you're seeking at,
and then concentrate on that, and then build your own network around that information,
and then analyze the resulting network.
The nice thing is, I have tons of questions, so I have many, many studies going on.
But for the purpose of this specific study,
oh, maybe I should also give you an overview of the category structure
and then come to the study at last.
As I said, every image sits in a very particular branch of the category.
The members are not allowed to choose more than one category when they're uploading an artwork.
Also, it's a way to make members really quite conscious about their actions.
You're not allowed to do batch uploading.
You can just upload only single images.
For each image, you have to choose a category that it should sit in,
give technical information.
Sometimes people just put dots, of course, but still,
really professional ones gives quite a lot of information there.
You also have to tag your own image with keywords, at least a few.
It makes members conscious that it's just a flicker where you're just batch uploading your holiday photos to share with your friends.
It's forcing you to be more professional about what you're doing in deviantart.
About the categories.
The red here, as may be expected, this is the biggest top category, it's photography.
Then it follows by digital art, then traditional art, then resources.
Then we move into smaller categories, which are actually, when I just call the names, you would say, aha.
For example, we have cartoons, manga and anime, customization, design and interfaces.
All these topics, actually, that are quite outside the classical art market, let's say, of today,
probably of the future as well.
But it would be more related to pop culture and would be more of an interest of a young group.
And deviantart, actually, if you just look at the demographics,
of course, the majority of the members are under 20, so we have a lot of teenagers.
So we have looked at the network structure, how the categories are connected to each other.
And here in this network figure, we have the nodes as categories.
The size of the nodes is correlated to how many members are uploading images in that given category.
So the way nodes, the categories are linked are, if a member is uploading images both in, let's say,
photography and digital art, then this information is used to link two categories to each other.
But we have, at the end of this network analysis, it was quite interesting to see that the light blue is,
has actually its own cluster there, the photography.
So kind of we are disregarding that it's the hugest part of the network.
And they are, of course, connected to each other.
But the interesting thing for us was to see that if we include the information not only on the top categories,
but on the second level, if you go deeper down the levels, you'll see that the fine granularity of the category structure
is actually based on technique.
And here we see that techniques are the things that actually keeps categories closer to each other
than the top categories where we can just talk about styles.
So someone who is publishing in digital art and traditional art might be connected through technique
rather than being really as expected to be really far away.
So where are we with our own case study, this one?
So our original question was, if we can follow how artistic content travels in this network.
Now first we use the word originality.
Then we discussed about the word originality and if it would be better to use the word maybe creativity.
It's not because we don't know what we are looking for, but it's more that when it comes to artworks,
you're actually swimming in dangerous waters.
I don't know if anybody would come up with a nice and easy definition of what art is or what you would call an artwork
and if everybody would also agree with you and call the same thing as an artwork.
But in general there is this feeling that an artwork should be something original, something that hasn't been done before.
So in that sense, how can I just follow up the flow if everything is original?
How can I just say, oh, this one is influenced by this?
And actually this is what we are interested in.
So at the end we have decided to use Eric Rogers' really very, very famous division of innovation theory
which he has suggested first in around 1960s and since then at one point it was almost becoming a discipline on its own.
So there are several case studies done since then about different groups, about different societies and communities
and how they react to when they face with something new.
Usually it's called innovation.
But we realize even innovation is not such an easy word to use in our setting
and there are quite differences to begin with with the typical setting that the theory is trying to be applied, usually applied successfully.
In our setting, as I said, everything should be original.
So for this simplicity sake, we opened the dictionary of course and looked at what does it stay for for innovation.
So here it is, it's the introduction of something new.
I think we hardly can talk about an artwork as an introduction of something new.
But the second definition was better fit, so a new idea, method or device.
So from method I just jumped to technique and that was actually something that is relatively easily traceable in deviantart.
And the reason of this is because we have such a nice category structure
and one of the biggest actually categories that we have is called resources.
So under resources, what we do see are images that are shared with other users.
So the majority of resources are stock images.
It makes kind of sense.
There are so many photographers in the website and with the digital era in photography,
you just take thousands of pictures and you really don't use every one of them.
So it's kind of easy way of sharing and making yourself a little bit also popular by sharing
because you just give full access to all the other members.
The small part of the resources was devoted to tutorials where people really take the time
and explain how they are using a certain technique.
So we thought we could follow both data sets and see what that will lead us at the end.
So this is just an example of a stock image.
Let's see, I'm really bad, I'll just show it to you with the mouse.
Okay, this one is the original and the three samples are reusing that original.
I think it's kind of easily recognizable here.
It's not so easy to see here as it's used in a flare and it's something here, I guess.
So the next example is a little bit easier to recognize.
This is the original stock image and you can see the figure in every one of them.
Maybe this one is a little bit manipulated.
So in general what you expect is, oh, we should be able to do it with image analysis.
So here I want to show you something from, I just prepared it for you.
Not this one, but this one.
Okay, could you tell me which one is the original image in this one?
Guesses?
I can buy chocolate at the end of the talk.
Yeah, I would give a plus or something, but since I'm not going to see you again,
chocolate would be more beneficial, I guess.
Okay, so actually this is the original image.
I think that just shows you that it's really not so easy.
With how it goes.
So that is why we have decided to use different techniques and a combination of different techniques.
Okay, we have used text analysis.
And the nice thing about, another nice thing about DeviantArt is it has a really nice etiquette.
So if someone is using somebody else's image, they usually leave a comment.
Thank you, I used it here and leave a link.
We just crawled for all the comments, kind of prepared a nice dataset where we have the originals
and the images that we know for sure is based on those originals.
Also, we also have the galleries of those members.
So think about it as an ego network for each guy who is publishing a stock inventory resource.
And this nice S-shaped curve, maybe I'll just come back later to it because I just have five minutes.
So we did some other analysis which just allowed us to see that DeviantArt also kind of follows
the same type of general curve that is expected in a community who is facing a new innovation.
Where you just have this S-curve where you have people who are just embracing the new idea.
And then only their closest neighborhood just follows up with them.
And all of a sudden you have kind of a rather sharp increase.
And the idea is diffused into the network and at the end you have the late adopters.
And we have seen something similar here, but of course we know that it's really not easy to make comparisons.
In case of DeviantArt, we really don't expect everyone to just use one technique only
or the whole population to adopt a technique.
That would be a paradigm shift and we really don't expect to see something like this.
Things like this happens every 100 years or so in art history.
So if I just wait another 90 years, maybe I'll see something, but I'm not expecting anything.
So the second step was network analysis.
This time we have just focused on the resources network.
And here the nodes are actually the images themselves.
And the connections are people who are favoring those images.
It was a very densely connected network with around 17,000 nodes,
but more than 1.5 million arcs.
It was crazy.
There is the score around 3,000 people who are publishing resources
and so many people watching them and following them.
So what we did was to take a snow sample
and then the resulting visualization showed us four clusters.
So photography was something that we have expected because, as I said,
photographers are publishing resources.
But what was really interesting for us to see is digital art and manga and anime.
And manga and anime, we expect that they are actually using the tutorials rather than stock images.
Stock images really do not fit into the image style of manga creation.
But we know that for manga creation there are really quite strict rules
that you have to follow and learn rather on your own.
So that's a really interesting hit there.
And the last image that I actually dread to talk about is the image analysis part.
We did image analysis earlier on smaller scales,
the figures that I have just run through,
where we know, for example, the number of members.
And we were not interested to see influences,
but rather to compare different styles.
But here what we want to see is direct links.
So instead of using the standard image analysis features,
we have tried something new.
It's called hog features and it's the state of the art in similarity-based image retrieval.
But we didn't have high hopes that truly get great results,
and we actually didn't get any nice results.
The results suck, actually. We decided not to use hog.
But the thing is, this feature is actually designed for,
for example, if you're doing an image search in Google,
and you're saying, okay, I want all the images that has a ball in it.
And then it works fine, because it doesn't differentiate between balls.
But what we are asking is, we have this original ball.
I want to have all the images that has the same ball.
And there's a problem that actually the ball is quite twisted and converted, might be.
Even the human eye cannot maybe say, oh, this is from that original image,
but I'm expecting the computer to find it for me.
So we have a problem, and luckily for us we have two years of finding a solution.
We have two years more grant money to sit on.
So our conclusions.
It just goes around the image analysis, because probably as an art historian,
I'm, but that's my main interest.
At the end, what we want to do is to come to such a stage,
where without the help of the metadata, without the help of network information,
or without the help of text analysis, et cetera, just in a given data set,
we would like to see how artistic content travels.
So that's the end goal, and we are just at the beginning of the research.
And thank you for listening.