IMPORTANT (02-Apr-2024)

Kaggle has fixed the issue with gzip files and Version 510 should now reflect properly working files

IMPORTANT (28-Mar-2024)

Please use the version 508 of the dataset, as 509 is broken. See link below of the dataset that is properly working https://www.kaggle.com/datasets/bwandowando/ukraine-russian-crisis-twitter-dataset-1-2-m-rows/versions/508

Context

The context and history of the current ongoing conflict can be found https://en.wikipedia.org/wiki/2022_Russian_invasion_of_Ukraine.

Announcement

[Jun 16] (🌇Sunset) Twitter has finally pulled the plug on all of my remaining TWITTER API accounts as part of their efforts for developers to migrate to the new API. The last tweets that I pulled was dated last Jun 14, and no more data from Jun 15 onwards. It was fun til it lasted and I hope that this dataset was able and will continue to help a lot. I'll just leave the dataset here for future download and reference. Thank you all!

[Apr 19] Two additional developer accounts have been permanently suspended, expect a lower throughtput in the next few weeks. I will pull data til they ban my last account.

[Apr 08] I woke up this morning and saw that Twitter has banned/ permanently suspended 4 of my developer accounts, I have around a few more but it is just a matter of time till all my accounts will most likely get banned as well. This was a fun project that I maintained for as long as I can. I will pull data til my last account gets banned.

[Feb 26] I've started to pull in RETWEETS again, so I am expecting a significant amount of throughput in tweets again on top of the dedicated processes that I have that gets NONRETWEETS. If you don't want RETWEETS, just filter them out.

[Feb 24] It's been a year since I started getting tweets of this conflict and had no idea that a year later this is still ongoing. Almost everyone assumed that Ukraine will crumble in a matter of days, but it is not the case. To those who have been using my dataset, i hope that I am helping all of you in one way or another. Ill do my best to maintain updating this dataset as long as I can.

[Feb 02] I seem to be getting less tweets as my crawlers are getting throttled, i used to get 2500 tweets per 15 mins but around 2-3 of my crawlers are getting throttling limit errors. There may be some kind of update that Twitter has done about rate limits or something similar. Will try to find ways to increase the throughput again.

[Jan 02] For all new datasets, it will now be prefixed by a year, so for Jan 01, 2023, it will be 20230101_XXXX.

[Dec 28] For those looking for a cleaned version of my dataset, with the retweets removed from before Aug 08, here is a dataset by @@vbmokin https://www.kaggle.com/datasets/vbmokin/russian-invasion-ukraine-without-retweets

[Nov 19] I noticed that one of my developer accounts, which ISNT TWEETING ANYTHING and just pulling data out of twitter has been permanently banned by Twitter.com, thus the decrease of unique tweets. I will try to come up with a solution to increase my throughput and signup for a new developer account.

[Oct 19] I just noticed that this dataset is finally "GOLD", after roughly seven months since I first uploaded my gzipped csv files.

[Oct 11] Sudden spike in number of tweets revolving around most recent development(s) about the Kerch Bridge explosion and the response from Russia.

[Aug 19- IMPORTANT] I raised the missing dataset issue to Kaggle team and they confirmed it was a bug brought by a ReactJs upgrade, the conversation and details can be seen here https://www.kaggle.com/discussions/product-feedback/345915 . It has been fixed already and I've reuploaded all the gzipped files that were lost PLUS the new files that were generated AFTER the issue was identified.

[Aug 17] Seems the latest version of my dataset lost around 100+ files, good thing this dataset is versioned so one can just go back to the previous version(s) and download them. Version 188 HAS ALL THE LOST FILES, I wont be reuploading all datasets as it will be tedious and I've deleted them already in my local and I only store the latest 2-3 days.

[Aug 10] 3/5 of my Python processes errored out and resulted to around 10-12 hours of NO data gathering for those processes thus the sharp decrease of tweets for Aug 09 dataset. I've applied an exception/ error checking to prevent this from happening.

[Aug 09] Significant drop in tweets extracted, but I am now getting ORIGINAL/ NON-RETWEETS.

[Aug 08] I've noticed that I had a spike of Tweets extracted, but they are literally thousands of retweets of a single original tweet. I also noticed that my crawlers seem to deviate because of this tactic being used by some Twitter users where they flood Twitter w...

10,000 tweets of people who mentioned Ukraine war between 2021 and 2022 May. Each **tweet **contains 4 types of data 1. Datetime 2. Tweet Id 3. Text 4. Username

Two formats .csv and .pkl are present

Social media can be mirrors of human interaction, society, and world events. Their reach enables the global dissemination of information in the shortest possible time and thus the individual participation of people all over the world in global events in almost real-time. However, equally efficient, these platforms can be misused in the context of information warfare in order to manipulate human perception and opinion formation. The outbreak of war between Russia and Ukraine on February 24, 2022, demonstrated this in a striking manner.

Here we publish a dataset of raw tweets collected by using the Twitter Streaming API in the context of the onset of the war which Russia started on Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia's invasion of Ukraine. We publish the IDs of all tweets we streamed during that time, the time we rehydrated them using Twitter's API as well as the result of the rehydration. If you use this dataset, please cite our related Paper:

Pohl, Janina Susanne and Seiler, Moritz Vinzent and Assenmacher, Dennis and Grimme, Christian, A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 (March 25, 2022). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543

The dataset includes Telegram channels with both pro-Kremlin and anti-Kremlin communications, collected over a timeframe covering one year prior to and one year following the Russian invasion. It consists of 404 pro-Kremlin channels featuring 4,109,645 posts and 114 anti-Kremlin channels containing 1,117,768 posts, all provided in JSON format. anti_kremlin_channel_list and pro_kremlin_channel_list encompasses details such as the channel name, username, Telegram link, and corresponding annotations. Important Note: For proper attribution, researchers who use this dataset in their work are invited to cite the following papers that describe this dataset and an example analysis.Bawa, A., Kursuncu, U., Achilov, D., Shalin, V. L., Agarwal, N., & Akbas, E. (2025). Telegram as a Battlefield: Kremlin-related Communications during the Russia-Ukraine Conflict. arXiv preprint arXiv:2501.01884.Bawa, A., Kursuncu, U., Achilov, D., & Shalin, V. L. (2024). the adaptive strategies of anti-kremlin digital dissent in telegram during the Russian invasion of Ukraine. arXiv preprint arXiv:2408.07135.

This dataset presents daily measurements of air pollution across more than 35 Ukrainian cities during the years 2024–2025, collected from official open government sources. Each row represents a specific measurement of an air pollutant (e.g., sulfur dioxide, ammonia, soot) in a given city on a particular day.

To enrich the contextual dimension of this dataset, we included open-source incident data on civilian harm resulting from missile and drone strikes.

🟢 Tasks Using Pollution Data (pollution_dataset.csv)

• 1️⃣ Time Series Forecasting: Air Pollution Prediction
• 2️⃣ Anomaly Detection in Air Quality (Detect unusual pollution spikes that deviate from normal patterns)
• 3️⃣ Clustering of Cities by Pollution Profiles

🔴 Tasks Using Civilian Harm Data (ukr_harm.csv)

• 4️⃣ NLP: Attack Event Categorization (Classify attack descriptions into predefined categories)
• 5️⃣ Location-Based Risk Modeling (Assess the likelihood of future attacks in specific cities based on past trends)

🟡 Tasks Using Both Datasets Together

• 6️⃣ Impact of War on Air Quality
• 7️⃣ Predicting Civilian Harm from Air Pollution Data
• 8️⃣ Spatio-Temporal Heatmaps of Pollution and Attacks

📂pollution_dataset.csv

Daily city-level air quality data from official Ukrainian sources | Feature | Description | | --- | --- | | Date | Observation date (YYYY-MM-DD) | | City | Ukrainian city name (in Ukrainian) | | CoordinateNumber | Station coordinate ID | | NameImpurity | Name of pollutant (translated to English) | | Value | Daily measurement of pollutant | | Civharm_id | Optional ID of conflict incident (if any) by date |

📌 Source: UKRAINIAN HYDROMETEOROLOGICAL CENTER OF THE STATE EMERGENCY SERVICE OF UKRAINE

📎 License: Creative Commons Attribution 4.0

📅 Coverage: Jan 2024 – Feb 2025

🗂 Format: Compiled from 14 CSV/XLSX monthly reports

📂 ukr_harm.csv

Crowdsourced database of attacks impacting civilians during the war

📌 Source: Bellingcat's Ukraine Civilian Harm Map.

🧾 Descriptions are based on open-source visual evidence (social media, geolocation, and video analysis)

⚠️ Disclaimer: Content may include references to violence or casualties; viewer discretion advised.

🔍 Data Cleaning & Standardization

Geolocation data was removed from ukr_harm.csv to protect privacy. Non-essential metadata was excluded. City names were standardized to match available locations in pollution_dataset.csv.

⚠️ Ethical Notice

This dataset was compiled for public research and academic analysis. The conflict layer may reference sensitive content, including descriptions of civilian casualties, destruction of infrastructure, and military-related terms.

➡️ Use respectfully and responsibly.

This is a dataset of videos and comments related to the invasion of Ukraine, published on TikTok by a number of users over the year of 2022. It was compiled by Benjamin Steel, Sara Parker and Derek Ruths at the Network Dynamics Lab, McGill University. We created this dataset to facilitate the study of TikTok, and the nature of social interaction on the platform relevant to a major political event.

The dataset has been released here on Zenodo: https://doi.org/10.5281/zenodo.7926959 as well as on Github: https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok

To create the dataset, we identified hashtags and keywords explicitly related to the conflict to collect a core set of videos (or ”TikToks”). We then compiled comments associated with these videos. All of the data captured is publically available information, and contains personally identifiable information. In total we collected approximately 16 thousand videos and 12 million comments, from approximately 6 million users. There are approximately 1.9 comments on average per user captured, and 1.5 videos per user who posted a video. The author personally collected this data using the web scraping PyTok library, developed by the author: https://github.com/networkdynamics/pytok.

Due to scraping duration, this is just a sample of the publically available discourse concerning the invasion of Ukraine on TikTok. Due to the fuzzy search functionality of the TikTok, the dataset contains videos with a range of relatedness to the invasion.

We release here the unique video IDs of the dataset in a CSV format. The data was collected without the specific consent of the content creators, so we have released only the data required to re-create it, to allow users to delete content from TikTok and be removed from the dataset if they wish. Contained in this repository are scripts that will automatically pull the full dataset, which will take the form of JSON files organised into a folder for each video. The JSON files are the entirety of the data returned by the TikTok API. We include a script to parse the JSON files into CSV files with the most commonly used data. We plan to further expand this dataset as collection processes progress and the war continues. We will version the dataset to ensure reproducibility.

To build this dataset from the IDs here:

1. Go to https://github.com/networkdynamics/pytok and clone the repo locally
2. Run pip install -e . in the pytok directory
3. Run pip install pandas tqdm to install these libraries if not already installed
4. Run get_videos.py to get the video data
5. Run video_comments.py to get the comment data
6. Run user_tiktoks.py to get the video history of the users
7. Run hashtag_tiktoks.py or search_tiktoks.py to get more videos from other hashtags and search terms
8. Run load_json_to_csv.py to compile the JSON files into two CSV files, comments.csv and videos.csv

If you get an error about the wrong chrome version, use the command line argument get_videos.py --chrome-version YOUR_CHROME_VERSION Please note pulling data from TikTok takes a while! We recommend leaving the scripts running on a server for a while for them to finish downloading everything. Feel free to play around with the delay constants to either speed up the process or avoid TikTok rate limiting.

Please do not hesitate to make an issue in this repo to get our help with this!

The videos.csv will contain the following columns:

video_id: Unique video ID

createtime: UTC datetime of video creation time in YYYY-MM-DD HH:MM:SS format

author_name: Unique author name

author_id: Unique author ID

desc: The full video description from the author

hashtags: A list of hashtags used in the video description

share_video_id: If the video is sharing another video, this is the video ID of that original video, else empty

share_video_user_id: If the video is sharing another video, this the user ID of the author of that video, else empty

share_video_user_name: If the video is sharing another video, this is the user name of the author of that video, else empty

share_type: If the video is sharing another video, this is the type of the share, stitch, duet etc.

mentions: A list of users mentioned in the video description, if any

The comments.csv will contain the following columns:

comment_id: Unique comment ID

createtime: UTC datetime of comment creation time in YYYY-MM-DD HH:MM:SS format

author_name: Unique author name

author_id: Unique author ID

text: Text of the comment

mentions: A list of users that are tagged in the comment

video_id: The ID of the video the comment is on

comment_language: The language of the comment, as predicted by the TikTok API

reply_comment_id: If the comment is replying to another comment, this is the ID of that comment

The date can be compiled into a user interaction network to facilitate study of interaction dynamics. There is code to help with that here: https://github.com/networkdynamics/polar-seeds. Additional scripts for further preprocessing of this data can be found there too.

This dataset contains data on 12K Reddit posts made to the r/UkraineRussiaReport subreddit. Information about sentiment (pro-Ukraine, pro-Russia, neither) was extracted from the post titles.

The dataset's sentiment labels are somewhat noisy. This is because post sentiment is classified by the author of a post.

Data was collected using Pushshift Reddit API during May 2023.

Each post includes information about: - post ID - pov (sentiment) - post title - score (upvotes) - author - number of comments - when the post was created

The USD/UAH exchange rate rose to 41.5504 on June 9, 2025, up 0.24% from the previous session. Over the past month, the Ukrainian Hryvnia has weakened 0.00%, and is down by 2.75% over the last 12 months. Ukrainian Hryvnia - values, historical data, forecasts and news - updated on June of 2025.

The dataset includes data collected from a series of public opinion polls conducted by the Kyiv International Institute of Sociology (KIIS) from May 2022 to February 2025, on the question aimed at measuring the readiness of the Ukrainian population for territorial concessions to end the war. The question used was: 'Which of these statements about possible compromises to achieve peace with Russia do you agree with more?' The answer options were: 'To achieve peace as quickly as possible and maintain independence, Ukraine may give up some of its territories,' or 'Under no circumstances should Ukraine give up any of its territories, even if it means the war will last longer and there will be threats to maintaining independence.' The background information includes respondents' socio-demographic profiles (gender, age, education, nationality, occupation, self-assessment of financial situation) and place of residence (oblast, type of settlement). The merged dataset includes data from 13 polls from May 2022 to February 2025 with a total of 18,215 respondents. All survey waves were conducted with samples representative of the adult population (18 years and older) of Ukraine (within the territories controlled by the Ukrainian government as of February 24, 2022) using the CATI (computer-assisted telephone interview) method. The question was asked to either the full sample (2,000 respondents) or a subsample (1,000 respondents), depending on the survey wave. The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook). The Data Documentation (pdf file) also includes a short overview and discussion of survey results.

New in version 1.1. The previous version (v1.0) contained data from the beginning of the monitoring (May 2022) up to May 2024. This version (v1.1) includes data from three new survey waves, extending the coverage period to February 2025.

The Gross Domestic Product (GDP) in Ukraine was worth 178.76 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Ukraine represents 0.17 percent of the world economy. This dataset provides the latest reported value for - Ukraine GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Understanding the dynamics of citizens' opinions, preferences, perceptions, and attitudes is pivotal in political science and essential for informed policymaking. Although highly sophisticated tools have been developed for analyzing these dynamics through surveys, outside the field of polarization, these analyses often focus on average responses, thereby missing important information embedded in other parameters of data distribution. Our study aims to fill this gap by illustrating how analyzing the evolution of both the mean and the distribution shape of responses can offer complementary insights into opinion dynamics. Specifically, we explore this through the French public's perception of defense issues, both before and after the onset of the war in Ukraine. Our findings underscore how routinely combining classical approaches with the use of existing tools for measuring distribution shapes can provide valuable perspectives for researchers and policymakers alike, by highlighting the nuanced shifts in public opinion that traditional methods might overlook.

This dataset contains selected columns of the datasets from the UkrAiA survey restricted to the age of 18-59 (N=995) conducted in 2022 in csv and STATA dta file formats. The selected variables accompany the paper "Attitudes of Ukrainian Refugees in Austria: Gender Roles, Democracy, and Confidence in International Institutions". Due to data confidentiality reaons, some variables were grouped (reflected in the variable name: _aggr).

The research projects Ukrainian Arrivals in Austria (UkrAiA) aimed to shed light on Ukrainian displaced persons in Austria who left their homes due to the Russian war of aggression. The project sought to establish an evidence base for understanding the needs and resources of displaced individuals in the areas of integration, education, labour market, housing personal values and beliefs. It was conducted by researchers from the Vienna University of Economics and Business (WU) and the Austrian Academy of Sciences (OeAW) in Austria.

The UkrAiA survey was a rapid-response survey and provided the first reliable data on Ukrainian displaced persons in Austria. The field phase took place between April and June 2022, during the early stages of the war. Data collection was carried out using a multi-mode approach (PAPI and CAWI) following convenience sampling. The final sample consisted of N=1,094 Ukrainian individuals aged 18 and above. The survey design was approved by the ethics committee of the Vienna University of Economics and Business and follows the university's as well as international refugee studies' ethical guidelines. During the field phase of the survey financial support was provided by the City of Vienna and the Vienna Social Fund. Furthermore, the University of Applied Sciences Salzburg supported the CAWI design.

The files will be made available as soon as the accompanying paper is published.

Inflation Rate in Ukraine increased to 15.10 percent in April from 14.60 percent in March of 2025. This dataset provides the latest reported value for - Ukraine Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The data has been collected by extracting hashtags from Twitter. The emphasis has been done on the Russia-Ukraine war and the underlying sentiment of people around the world who have posted their thought in Twitter. So, in this context the below mentioned hashtags have been selected to fetch the data from the tool: 1. #ukrainewar 2. #russianattack 3. #russian navy 4. #russianarmy 5. #prayforukraine 6. #NATO 7. #SaveUkraineNow 8. #ukraineunderattack 9. #ukrainecrisis 10. #StopPutinNOW 11. #ukraineconflict 12. #StopTheWar 13. #StopRussia

To extract the sentiment, TextBlob (for the extraction of polarity and subjectivity) can be used to for text mood analysis, text2sentiment module can be applied.

Since 2020, the world has faced two unprecedented shocks: lockdowns (regulation) and the invasion of Ukraine (war). Although we realise the health and economic effects of these shocks, more research is needed on the effect on happiness and whether the type of shock plays a role. Therefore, in this paper, we determine whether these macro-level shocks affected happiness, how these effects differ, and how long it takes for happiness to adapt to previous levels. The latter will allow us to test whether adaptation theory holds at the macro level. We use a unique dataset of ten countries spanning the Northern and Southern hemispheres derived from tweets extracted in real-time per country. Applying Natural Language Processing, we obtain these tweets’ underlying sentiment scores, after which we calculate a happiness score (Gross National Happiness) and derive daily time series data. Our Twitter dataset is combined with Oxford’s COVID-19 Government Response Tracker data. Considering the results of the Difference-in-Differences and event studies jointly, we are confident that the shocks led to lower happiness levels, both with the lockdown and the invasion shock. We find that the effect size is significant and that the lockdown shock had a bigger effect than the invasion. Considering both types of shocks, the adaptation to previous happiness levels occurred within two to three weeks. Following our findings of similar behaviour in happiness to both types of shocks, the question of whether other types of shocks will have similar effects is posited. Regardless of the length of the adaptation period, understanding the effects of macro-level shocks on happiness is essential for policymakers, as happiness has a spillover effect on other variables such as production, safety and trust.

Abstract

Starting in February 2022, an increasing number of refugees and third-country nationals (TCNs) entering Slovakia was registered as a result of the war in Ukraine. As of 25 May 2022, Slovak authorities reported over 450,000 arrivals from Ukraine out of whom close to 420,000 were Ukrainian refugees and close to 14,000 TCNs. Data collected by REACH between March and June 2022 also indicated that 38% of respondents crossing the border into Slovakia considered it as their final destination and that 65% of those intended to stay in the country as long as the conflict in Ukraine continued. To respond to their needs, collective centers (CCs) were to host the refugees. While these centers play a key role in the humanitarian response, the overwhelming majority of refugees reside in the host community; however, little to no information is currently available (May 2022) to response actors regarding their demographic profile, household composition, geographical presence, vulnerabilities, humanitarian needs, movement intentions, or coping capacities.

In this context, REACH with the support of UNHCR undertook a multi-sector needs assessment (MSNA) light with the global objective of supporting an evidence-based humanitarian response in Slovakia through the provision of multi-sectoral data about the needs and coping capacities of Ukrainian refugee households in the country. Data collection took place between 18 July and 12 August2022. This dataset is the anonymous version of the original dataset. Note that the variable nationality was removed from the dataset to protect data subjects, however all but one respondent was of Ukrainian nationality.

Geographic coverage

All 8 regions of the Slovak Republic

Analysis unit

Households

Universe

Persons of concerns (PoCs) to UNHCR living inside collective centres (2,149 individuals / 611 households) and outside collective centres (76,452 individuals / 21,844 households) in the Slovak Republic.

Kind of data

Sample survey data [ssd]

Sampling procedure

Households living inside collective centres were purposively selected, and thus are not statistically representative. Households living outside of collective centres were selected using a two-stage random sampling (not stratified).

Mode of data collection

Face-to-face [f2f]

More than 8 million refugees have fled Ukraine since the escalation of conflict on 24 February 2022. The number of people crossing fluctuated greatly in the early onset of the war in Ukraine.

To understand the drivers of displacement and intentions of refugees, over 18,000 interviews were conducted with people crossing from Ukraine into Poland, Hungary, Moldova and Slovakia. Interviews were conducted at several border checkpoint and certain reception centers, and began on 28 February. Interviewees were selected purposively to gain a broader understanding of experiences and intentions, and results should therefore be considered indicative.

This dataset is the anonymized version of the original data.

This dataset is a product generated to track the change of migrant numbers from Ukraine since the war began in 2023-02-05.This data provides the percent change of population detected from Facebook users compared to a pre-war baseline for the same administrative unit. For more information about the Facebook data, please refer to the Population Maps page from Data for Good at Meta.How was the pre-event baseline calculated?The pre-war baseline was calculated as an average over a 90-day time window prior to the earthquake event (2023-02-05).Key metricsPercent change between current and baseline. Change in percentage between the trackable population by Facebook of the current date and the baseline period.Baseline FB users. Anonymized and aggregated Facebook users that are trackable (consent to be included in the dataset) of 90 days before the event.