 Hello everyone, my name is Piotr Konieczny and today on behalf of myself and my co-author Wodzimierz Lewoniewski, I will tell you a bit about measuring Americanization, which is so by comparing the amount of content related to the United States in different language versions of Wikipedia and the popularity of those pages, we can measure the degree of unrecognization worldwide. And again, this is what I think our, let's say, big contribution is to quantify those previously assumed but never reconfirmed hypotheses. Now, in part of what gave us a lot of trouble at the beginning was deciding how to measure this. Again, people tend to think a lot about Wikipedia, and it is a wonderful category structure, but the category structure is actually not ideal. It has some problems, this structure is complex, and for various reasons that are described in our big paper, and we don't have the time to go over here, we decided to go with Wikidata, where the data is much more structured, three of many errors, human originality speaking, that plug Wikipedia. So Wikidata contains statements that can be used to determine, after some cleanup, whether a particular object that generally means an article for us from different languages of Wikipedia is connected to a particular culture, country, or language. Now, there are many issues that we had to deal with that includes, among other things, issues such as some countries don't have an associated language, for example, India, a very big populist country, there's no Indian language. And on the other hand, English Wikipedia is actually used by United States, United Kingdom, Ireland, and many other countries. Same for the Spanish, Portuguese, Arabic Wikipedia. So there are a number of issues, and we developed quite a lot of discussion in our big paper, associated limitations. But let's move on to the more fun issues. We used 90 million Wikidata items or so. We used some previous research and theories developed by Michael Ribba and Laniado, as well as some of my own discussions and developments based on so-called Engelhardt's World Cell Cultural Clusters model. You may be familiar with it. It sometimes even makes it to the newspapers and so on. It's the one where there are the two dimensions to access X, Y, and various countries are mapped and cultural clusters like the English speaking and Orthodox and Islamic countries and so on and so on. Now, in our research, we developed several parameters such as how many people in a given country are actually reading the Wikipedia. That's an issue because, for example, in small countries, many people go and read another Wikipedia, rather than, for example, if you look at the countries such as Estonia or Norway, a lot of people there speak English. There are actually lots of small countries. Many people will, instead of developing their own language, Wikipedia, will just go to the English ones. This is one of those issues we had to deal with. We also looked at things like how many people in that country are looking at Wikipedia. And then also we looked at how many articles related to the United States there are and how many views of those articles there are. Because just looking at the article number would not be everything. For example, let's say Swedish Wikipedia had a boat running that translated like a gigantic number of articles about villages. That doesn't mean people really care about those tiny villages around the world, including Americans ones. So the point is, let's say the Swedish Wikipedia will have a higher percentage of possibly articles related to American villages than others. But it doesn't mean, again, that Swedish people care about those American villages more than somebody in other countries. So we try to control for this. And I will park here on our pretty much final slide with some content that I think you will find interesting. What we see here is a bubble jar at the table. The table has displays data, the statistics I mentioned, about the biggest pretty much Wikipedia languages. Our full table would be like something like 60 entries wouldn't be very visible. So we just displayed things here related to the most popular languages. And as for the bubble chart, what the X axis is the United States related to Wikipedia article share. And the Y axis are the views that are related to that share. And the size of the bubbles talks about another parameter. This is the number of articles in that Wikipedia language. So look at the big Wikipedia. So I have big bubbles, small Wikipedia have small bubbles. So this is really what we are looking at here. Now we can observe that the big blue languages have higher value of the US RAS. In other words, the parameter we call again here, United States related articles just to remind people what we are talking about. And often those Wikipedia's are again bigger. Well, that's the reason why they are bigger is a bit outside our research is again related to digital divide to some socioeconomic factors, but also culture. I mean, this is fascinating topic that I am really running out of time, I seem to discuss. But what we can conclude we can conclude here that there is indeed this correlation that Western culture roughly defined Western countries have somewhat more more of an interest in American topics. Of course, the English Wikipedia, as you can see, is the biggest one. And this is the obvious common sense test. For example, obviously, Americans, for example, read in generally in English, and they are the ones who are most interested in American topics. So this is like the common sense test. If we are checking, let's say for Estonian coverage of Estonian topics, then Estonians would be probably the most interested in this. But again, you can see also that what are some of the other languages where people are very interested in those topics. Italy, for example, Spain, Portugal, France, those, again, this, then Germany, Russia a bit less, but still. So again, we can see things that generally align themselves with the view that Western cultures are more interested a bit than non-Westerns. For example, let's say the Vietnamese Wikipedia is not as interested in American topics as or the Thai Japanese as again, Spanish or Italian. So this is something that I think is quite interesting here. And again, you can say everybody knew that this is the case. Well, people suspected, but one of the big things we want to show is that now we can use the weak data. We can use the data from the collective intelligence of Wikipedia to actually prove those things that people suspected about the world. And well, this is, I think, is quite interesting. We can now measure how popular European, Chinese or, you know, things related to biology are across different dimensions. And Wikipedia allows us to do so. All right.