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This paper examines Russia's propaganda discourse on Twitter during the 2022 invasion of Ukraine. The study employs network analysis, natural language processing (NLP) techniques, and qualitative analysis to identify key communities and narratives associated with the prevalent and damaging narrative of "fascism/Nazism" in discussions related to the invasion. The paper implements a methodological pipeline to identify the main topics, and influential actors, as well as to examine the most impactful messages in spreading this disinformation narrative. Overall, this research contributes to the understanding of propaganda dissemination on social media platforms and provides insights into the narratives and communities involved in spreading disinformation during the invasion. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The outbreak of conflict between Russia and Ukraine on February 23, 2022, was a huge shock, and it quickly became the dominant topic of conversation on social media. Our research is based on Twitter conversations about the conflict between Russia and Ukraine. This paper intends to monitor and analyze the Twitter conversation on the continuing war in Ukraine using natural language processing techniques such as word cloud analysis, sentiment analysis, and visualization. The major goal is to acquire insight into Twitter users' shifting thoughts and opinions on various parts of the conflict over time. The study's findings have important implications for politicians, journalists, and social media users who want to follow and understand how online communities' attitudes on global issues change. We hope that this study will help to gain a deeper knowledge of how social media works in affecting public debate and how it might be used to gain insights into public opinion on crucial global issues. In this work logistic regression, K-nearest neighbours and decision tree algorithm are used to analyse the sentiments and got classification accuracy of 94.58%, 88.89% and 90.45% respectively. [ABSTRACT FROM AUTHOR]

• War attention is an important driver of stock volatility. • The war attention index uses NLP and dimensionality reduction. • New GARCH-MIDAS models, adding war-attention extreme effects, are used. • War attention index enhances S&P500 volatility predictability. This paper aims to explore the impact of war attention on stock volatility predictability by constructing a new war attention index and employing an extended GARCH-MIDAS-ES model. The war attention index is developed by incorporating the Google search volume data for 56 war-related keywords using natural language processing methods and dimensionality reduction techniques. Since war attention is considered an exogenous shock, we modify the new extended MIDAS model by incorporating the extreme effects caused by war attention into the GARCH-MIDAS-ES framework. Compelling evidence demonstrates that our proposed war attention index is a statistically significant driver of S&P 500 volatility, and our extended model exhibits higher out-of-sample predictive accuracy as it captures both the normal and extreme effects of war attention on stock volatility within the MIDAS framework. By examining how war attention affects stock volatility predictability during the ongoing Russia–Ukraine war, we observe that the extended model's forecasting performance deteriorates as the forecasting horizon increases to a relatively large extent, which is in line with the findings of Andrei and Hasler (2015). [ABSTRACT FROM AUTHOR]

The development of the internet and digital technologies has inadvertently facilitated the huge disinformation problem that faces society nowadays. This phenomenon impacts ideologies, politics and public health. The 2016 US presidential elections, the Brexit referendum, the COVID-19 pandemic and the Russia-Ukraine war have been ideal scenarios for the spreading of fake news and hoaxes, due to the massive dissemination of information. Assuming that fake news mixes reliable and unreliable information, we propose RUN-AS (Reliable and Unreliable Annotation Scheme), a fine-grained annotation scheme that enables the labelling of the structural parts and essential content elements of a news item and their classification into Reliable and Unreliable. This annotation proposal aims to detect disinformation patterns in text and to classify the global reliability of news. To this end, a dataset in Spanish was built and manually annotated with RUN-AS and several experiments using this dataset were conducted to validate the annotation scheme by using Machine Learning (ML) and Deep Learning (DL) algorithms. The experiments evidence the validity of the annotation scheme proposed, obtaining the best F 1 m , 0.948, with the Decision Tree algorithm. [ABSTRACT FROM AUTHOR]