 Hello everyone, my name is Ziad Damak, and today I'm going to present the project Effects of the Russian-Ukrainian War on the Editorial Activity of the Ukrainian, Russian and English Wikipedia. As we all know, Wikipedia is a leading resource of information that plays an important role in the spread of news, events and social movements. And during the time of process, this volunteer-based model could be influenced and which lead to potential discrepancy among the users. So mainly this research focuses on studying the behavior of such entities during the Russian-Ukrainian War between 2020-2022, and we're going to perform an in-depth analysis of the data and in order to gain insights into the dynamics of the platform and the communities. Now we move on to our research questions. The first one would be, how does the activity change for editors and viewers during the war? So we start from the pre-process to the set, which is extracted from the Wikipedia media Wiki history dumps from the period of January 2020 till June 2022. Then we were going to perform a difference in difference analysis in order to get an overall insight of the English, Russian and Ukrainian Wikipedia. And this methodology is replicated from the previous work of Rupert Sturantov in 2021 on the COVID-19 pandemic. The second research question would be, how does the collaboration between editors change? So first we start by pre-processing our data. So we extracted it from the Wikipedia API, which contains articles and revisions and comments. And our data would be divided into three main groups, which are the related articles to the main page of the war, the K-top viewed articles and K-random articles. Afterwards we're going to perform a reverse analysis in order to investigate potential edit words. Now we move on to our experiment result for the first research question and specifically for the edit volume. So we can see here that there is a significant decrease in activity near the date of the invasion in the Russian and Ukrainian Wikipedia. For the Ukrainian Wikipedia, the level ultimately surpasses the pre-invasion activity. This observed recovery in activity for both of the Russian and Ukrainian Wikipedia indicates that there is a resilience of the communities contributing to Wikipedia. For the English Wikipedia, we didn't see any significant change. Now we move on to our second experiment, which is about the contributed information. We can see here that there is an increase in both of the Ukrainian and Russian Wikipedia's in terms of contributed information symbiotes, which is also confirmed by the difference in difference analysis. So this addition of large volume of information is due to numerous events and changes appearing during the war. Also when comparing the contributed information metric to the edit volume metric, we can see that the Russian and Ukrainian edits remains consistent with 2021 and 2020, but there is a significant increase in terms of contributed information in these languages. The same thing for the English Wikipedia, we didn't see any significant change. Now regarding the revert rate, we can see that there is a spike in the revert rate for both of the languages and specifically for the Russian Wikipedia, which indicates a disagreement and a lack of consensus in the early stages of the war and specifically in the first 15 days. Now our next experiment is the reverse analysis in the context of the related articles. We found here that the Russian Wikipedia exhibited a spike in terms of revert rate and this high volume of revert rate for this language indicates that the political context is really complex and it may indicate that there is a high level of disagreement among the editing community leading to polarized editors. Now for our main findings first, we have found that there is a short term decrease in the number of edits and a smaller decrease in the aggregated contributed information, other than that we found that the Wikipedia editor communities are overall resilient in the times of the war and also there are some potential indications of polarized community inside the Russian Wikipedia and finally we have investigated the number of newcomers also, the readership data and finally we have explored some existing analysis approaches in our project. Thank you everyone and feel free to ask any question.