Sentiment analysis of Twitter data related to the Russian-Ukraine war involves using natural language processing techniques to analyze the sentiments expressed in tweets about the ongoing conflict between Russia and Ukraine. The analysis involves identifying and categorizing the emotions expressed in the tweets, such as positive, negative, or neutral, and analyzing the overall sentiment of the tweets.

The analysis can provide insights into the public sentiment towards the conflict, as well as the various parties involved in the conflict, such as Russia, Ukraine, and other international players. The sentiment analysis can also help identify trends and patterns in the sentiment over time, such as changes in sentiment towards the conflict during specific events or periods.

Some of the key features of sentiment analysis of Twitter data related to the Russian-Ukraine war include data collection and preprocessing, sentiment classification, and data visualization. These features enable businesses, organizations, and governments to gain valuable insights into public sentiment towards the conflict, and to use this information to inform their decision-making processes.

Overall, sentiment analysis of Twitter data related to the Russian-Ukraine war is a powerful tool for understanding public sentiment towards the conflict and can help businesses, organizations, and governments make informed decisions about their involvement in the conflict

The number of Facebook users in Russia increased by *** million users (+**** percent) in 2021 in comparison to the previous year. Therefore, the Facebook user base in Russia reached a peak in 2021 with ***** million users. Notably, the Facebook user base in this industry continuously increased over the last years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Facebook users in countries like Eastern Europe and Northern Europe.

The dataset consists of content published in groups of Russian universities on the social media VKontakte. The dataset contains posts and comments from 9,215 university publics from June 2022 to August 2023.

This report examines the extent to which Canadians are exposed to and might be influenced by pro-Kremlin propaganda on social media based on a census-balanced national survey of 1,500 Canadians conducted between May 12–31, 2022. Among other questions, the survey asked participants about their social media use, news consumption about the war in Ukraine, political leanings, as well as their exposure to and belief in common pro-Kremlin narratives.

The data set contains posts from social media networks popular among Russian-speaking communities. Information was searched based on pre-defined keywords ("war", "special military operation", etc.) and is mainly related to the ongoing war in Ukraine with Russia. After a thorough review and analysis of the data, both propaganda and fake news were identified. The collected data is anonymized. Feature engineering and text preprocessing can be applied to obtain new insights and knowledge from this data set. The data set is useful for the study of information wars and propaganda identification.

Context

This data was created using Twitter's publicly available Russian information operations datasets as well as legitimate users scraped from Twitter's API and filtered for bots using the Botometer API.

Content

The user csv contains identifying user information fields created from their tweets as well as a column with a Bag of Words created from the aggregate of their tweet content. The tweet csv contains a sample of 2000-3000 tweets per user. The legitimate user tweets are primarily from 2020, while the Russian information operations tweets primarily range from 2014-2017. ### Context

This data was created using Twitter's publicly available Russian information operations datasets as well as legitimate users scraped from Twitter's API and filtered for bots using the Botometer API.

Content

The user csv contains identifying user information fields created from their tweets as well as a column with a Bag of Words created from the aggregate of their tweet content. The tweet csv contains a sample of 2000-3000 tweets per user. The legitimate user tweets are primarily from 2020, while the Russian information operations tweets primarily range from 2014-2017. All identifying user information has been hashed for anonymity.

Dataset Overview

This dataset consists of scraped sentiment data from Twitter (X) and Reddit related to the Russia-Ukraine conflict.

Data Collection Methodology

• Tool Used: SNScrape

• Platforms**:Twitter (X) and Reddit

• Sample Size: Twitter: Approximately 10,000 tweets Reddit: Approximately 11,000 comments

• Time Period: January 2022 to April 2023

Data Collection Strategy

• Twitter Data Divided into six time periods to avoid temporal bias Captures sentiment trends over the duration of the conflict Ensures representation of evolving public opinion

** Search Parameters**

Keywords used for data collection included: "Russia Ukraine war" "war in Ukraine" "Russia invades Ukraine" "Ukraine war"

Note to Users

This dataset provides a comprehensive view of public sentiment on social media regarding the Russia-Ukraine conflict. It's designed to support various analyses, including sentiment analysis, trend identification, and public opinion research.

The number of social media users in Eastern Europe was forecast to continuously increase between 2024 and 2029 by in total 40.5 million users (+23.11 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 215.71 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of social media users in countries like Central & Western Europe and Russia.

Techsalerator’s Location Sentiment Data for Russia

Techsalerator’s Location Sentiment Data for Russia provides valuable insights into how people perceive various locations across the country. This dataset is essential for businesses, researchers, and policymakers looking to analyze public sentiment, social trends, and economic factors at a regional and national level.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Top 5 Key Data Fields

• Geographical Location – Identifies the city, region, or district for precise sentiment analysis.
• Sentiment Score – Measures positive, neutral, or negative sentiment levels using advanced NLP techniques.
• Source of Sentiment – Categorizes sentiment sources such as social media, news, reviews, and surveys.
• Timeframe of Sentiment – Provides timestamps to track sentiment shifts over time.
• Demographic Breakdown – Analyzes sentiment by age, gender, profession, and other key demographics.

Top 5 Location Sentiment Trends in Russia

• Urban vs. Rural Sentiment Divide – Larger cities like Moscow and St. Petersburg show higher economic optimism, while rural areas exhibit concerns over infrastructure and job opportunities.
• Political and Social Sentiment Variability – Public sentiment shifts in response to government policies, economic sanctions, and international relations.
• Tourism and Cultural Sentiment – Foreign visitors and locals express strong opinions about major tourist destinations, affecting travel trends.
• Consumer Behavior Insights – Sentiment data reveals trends in shopping, dining, and entertainment preferences across different regions.
• Environmental Concerns – Increasing public discussions about climate change, pollution, and sustainability, influencing governmental and corporate policies.

Top 5 Applications of Location Sentiment Data in Russia

• Market Research & Business Strategy – Helps companies understand consumer attitudes and improve location-based marketing strategies.
• Urban Planning & Development – Assists policymakers in making data-driven decisions to enhance public services and infrastructure.
• Election Campaigns & Political Analysis – Supports candidates and analysts in gauging public opinion on policies and governance.
• Crisis Management & Risk Assessment – Enables organizations to monitor sentiment fluctuations and respond to crises effectively.
• Tourism & Hospitality Industry – Provides insights into tourist satisfaction and areas for improvement in the travel sector.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Russia, contact info@techsalerator.com with your specific requirements. Techsalerator offers customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographical Location
• Sentiment Score
• Source of Sentiment
• Timeframe of Sentiment
• Demographic Breakdown
• Public Opinion on Key Topics
• Industry-Specific Sentiment Insights
• Economic Sentiment Indicators
• Tourism & Hospitality Sentiment Analysis
• Contact Information

For a comprehensive understanding of public perception and sentiment trends across Russia, Techsalerator’s dataset is a critical resource for businesses, researchers, and decision-makers.

Dataset Card for Dataset Name

The Russian Trolls twitter dataset as released and reported by NBC News. From the original data file header: "Tweets from confirmed Russian trolls, shows only username, timestamp (in UTC), tweet text, and number of times tweet was retweeted and favorited according to our data",,,,,,,,,,,,,,,,, From NBC News' story: https://www.nbcnews.com/tech/social-media/now-available-more-200-000-deleted-russian-troll-tweets-n844731,,,,,,,,,,,,,,,,, "If you publish… See the full description on the dataset page: https://huggingface.co/datasets/Kristijan/russian_trolls.

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

Data includes the results of the research "Social Network Analysis for Subnational Units’ External Relations of Russia". The dataset consists of 1) Annex with saved data and the script for modelling and ploting results in Rstudio; 2) Annex with the working space to reproduce of measurements in Rstudio; 3) Annex with saved space in Gephi; 4) Annex with plots used in the research paper

Russian Athletes do not understand why they were rejected from almost all competitions. We can tell them why on Social Media. You know what you should do. The data sources are Paris Olympic 2024, Beijing Olympic 2022, and Olympics.
Instagram, facebook, vk, twitter, and youtube are included. Stand with Ukraine.

Dataset Description

This is the dataset used in a study of Russian verbal loans in Udmurt. The files contain lists of Russian verbs found in the Udmurt social media corpus (http://udmurt.web-corpora.net/index_en.html), manually annotated for several features such as aspect or frequencies in different corpora. Abstract: In Udmurt, a Uralic language that has experienced long and extensive contact with the dominant Russian language, all four typologically relevant strategies of verbal borrowing are attested. This is unusual both cross-linguistically and for the Uralic family. The paper investigates these strategies and the factors that govern their choice. It turns out that, although free variation plays a major role in the distribution of strategies, there are also several important morphological, stylistic and areal factors. By analyzing these factors and the available historical data, I propose a diachronic explanation of the currently observed distribution. The study is mostly based on corpus data collected from contemporary Udmurt-language social media.

TL;DR: Text Normalization for Social Media Corpus

  Dataset Description

This dataset contains examples of Russian-language texts from social networks with distorted spelling (typos, abbreviations, etc.) and their normalized versions in json format. A detailed spelling correction protocol is given in the TBA article. The dataset size is 1930 sentence pairs. In each pair, the sentences are tokenized by words, and the lengths of both sentences in the pair are equal. If a… See the full description on the dataset page: https://huggingface.co/datasets/ruscorpora/normalization.

A Russian-language sentiment lexicon for social media discussions on political and social issues.

The file contains raw markings collected with LINIS coding service https://linis-crowd.org [in Russian].

Learn more about PolSentiLex in our papers:

Koltsova, O., & Alexeeva, S. (2015). Linis-crowd.org: A lexical resource for Russian sentiment analysis of social media [Linis-crowd.org: Lexichesk resurs dl’a analiza tonal’nosti sotsial’no-politicheskix tekstov]. Computational Linguis- Tics and Computantional Ontologies: Proceedings of the XVIII Joint Conference “Internet and Modern Society (IMS-2015)” [Kompyuternaya Lingvistika i Vyichis- Litelnyie Ontologii: Sbornik Nauchnyih Statey. Trudyi XVIII Ob’edinennoy Konferen- Tsii «Internet i Sovremennoe Obschestvo» (IMS-2015)], 25–34. [in Russian] URL: https://scila.hse.ru/data/2020/06/02/1603986481/koltsovaoyuetal.pdf

Koltsova, O., Alexeeva, S., & Koltsov, S. (2016). An Opinion Word Lexicon and a Training Dataset for Russian Sentiment Analysis of Social Media. Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2016”, 277–287. URL: http://www.dialog-21.ru/media/3400/koltsovaoyuetal.pdf

Koltsova O., Alexeeva S., Pashakhin S., Koltsov S. (2020) PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media. In: Filchenkov A., Kauttonen J., Pivovarova L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. https://doi.org/10.1007/978-3-030-59082-6_1

Dataset

This dataset was created by Ananya Luthra

Contents

DEPRECATED: This resource has been split into two parts - text and speech and moved to huggingface for convenience

The dataset consists of two parts: 1. The tsv file with anecdotes themselves and metadata (publishing and access timestamps, number of likes and views); 2. The tar.xz file with automatically generated speech representation of the anecdotes.

Toxic Russian Comments Dataset

This dataset contains labelled comments from the popular Russian social network ok.ru.

The data was used in a competition where participants had to automatically label each comment with at least one of the four predefined classes. The classes represent different levels of toxicity. The competition was held on the All Cups platform.

Each comment belongs to one of the following classes, with each label complying with the fastText formatting rules:

• _label_NORMAL - neutral user comments

• _label_INSULT - comments that humiliate a person

• _label_THREAT - comments with an explicit intent to harm another person

• _label_OBSCENITY - comments that contain a description or a threat of a sexual assault

Data overview:

count_of_elements: 248290
count_of_labels: 4
label_count:
 _label_NORMAL: 203685
 _label_INSULT: 28567
 _label_INSULT,_label_THREAT: 6317
 _label_THREAT: 5460
 _label_OBSCENITY: 2245
 _label_INSULT,_label_OBSCENITY: 1766
 _label_INSULT,_label_OBSCENITY,_label_THREAT: 176
 _label_OBSCENITY,_label_THREAT: 74

Techsalerator's News Events Data for Russia: A Comprehensive Overview

Techsalerator's News Events Data for Russia offers a valuable resource for businesses, researchers, and media organizations. This dataset compiles information on significant news events across Russia, drawing from a broad spectrum of media sources including news outlets, online publications, and social platforms. It provides essential insights for those interested in tracking trends, analyzing public sentiment, or monitoring industry-specific developments.

Key Data Fields - Event Date: Records the exact date of the news event. This is important for analysts tracking trends over time or businesses responding to market shifts. - Event Title: A concise headline describing the event. This allows users to quickly categorize and assess news content based on relevance to their interests. - Source: Indicates the news outlet or platform where the event was reported. This helps users track credible sources and evaluate the reach and influence of the event. - Location: Provides geographic details, showing where the event occurred within Russia. This is particularly useful for regional analysis or localized marketing efforts. - Event Description: A detailed summary of the event, outlining key developments, participants, and potential impact. Researchers and businesses use this to understand the context and implications of the event.

Top 5 News Categories in Russia - Politics: Major coverage on government decisions, political movements, elections, and policy changes affecting the national landscape. - Economy: Focuses on Russia’s economic indicators, inflation rates, international trade, and corporate activities influencing business and finance sectors. - Social Issues: News events related to public protests, health issues, education, and other societal concerns driving public discourse. - Sports: Highlights events in popular sports like football and ice hockey, often drawing significant attention and engagement across the country. - Technology and Innovation: Reports on tech developments, startups, and innovations within Russia’s expanding tech ecosystem, featuring emerging companies and advancements.

Top 5 News Sources in Russia - RIA Novosti: A major news agency providing comprehensive coverage of national politics, economy, and social issues. - TASS: Russia’s national news agency known for its extensive updates on breaking news, politics, and current affairs. - Kommersant: A widely-read newspaper offering insights into local politics, economic developments, and societal trends. - RT (Russia Today): An international news network covering a wide range of topics including politics, economy, and global affairs. - Vedomosti: A prominent business daily known for its analysis of economic developments, market trends, and corporate news.

Accessing Techsalerator’s News Events Data for Russia To access Techsalerator’s News Events Data for Russia, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields - Event Date - Event Title - Source - Location - Event Description - Event Category (Politics, Economy, Sports, etc.) - Participants (if applicable) - Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is an essential tool for keeping track of significant events in Russia. It aids in making informed decisions, whether for business strategy, market analysis, or academic research, providing a comprehensive view of the country’s news landscape.