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Policymakers and researchers worldwide are interested in measuring the subjective well-being (SWB) of populations. In recent years, new approaches to measuring SWB have begun to appear, using digital traces as the main source of information, and show potential to overcome the shortcomings of traditional survey-based methods. In this paper, we propose the formal model for calculation of observable subjective well-being (OSWB) indicator based on posts from a social network, which utilizes demographic information and post-stratification techniques to make the data sample representative by selected characteristics of the general population. We applied the model on the data from Odnoklassniki, one of the largest social networks in Russia, and obtained an OSWB indicator representative of the population of Russia by age and gender. For sentiment analysis, we fine-tuned several language models on RuSentiment and achieved state-of-the-art results. The calculated OSWB indicator demonstrated moderate to strong Pearson's ( r = 0.733 , p = 0.007 , n = 12 ) correlation and strong Spearman's ( r s = 0.825 , p = 0.001 , n = 12 ) correlation with a traditional survey-based Happiness Index reported by Russia Public Opinion Research Center, confirming the validity of the proposed approach. Additionally, we explored circadian (24 h) and circaseptan (7 day) patterns, and report several interesting findings for the population of Russia. Firstly, daily variations were clearly observed: the morning had the lowest level of happiness, and the late evening had the highest. Secondly, weekly patterns were clearly observed as well, with weekends being happier than weekdays. The lowest level of happiness occurs in the first three weekdays, and starting on Thursday, it rises and peaks during the weekend. Lastly, demographic groups showed different levels of happiness on a daily, weekly, and monthly basis, which confirms the importance of post-stratification by age group and gender in OSWB studies based on digital traces. [ABSTRACT FROM AUTHOR]

In this paper, we assess sentiment and emotion regarding green energy through employing a social listening analysis on Twitter. Knowing the sentiment and attitude of the population is important because it will help to promote policies and actions that favor the development of green or renewable energies. We chose to study a crucial period that coincides with the onset of the 2022 Ukrainian–Russo conflict, which has undoubtedly affected global energy policies worldwide. We searched for messages containing the term "green energy" during the days before and after the conflict started. We then performed a semantic analysis of the most frequent words, a comparative analysis of sentiments and emotions in both periods, a dimensionality reduction analysis, and an analysis of the variance of tweets versus retweets. The results of the analysis show that the conflict has changed society's sentiments about an energy transition to green energy. In addition, we found that negative feelings and emotions emerged in green energy tweeters once the conflict started. However, the emotion of confidence also increased as the conflict, intimately linked to energy, has driven all countries to promote a rapid transition to greener energy sources. Finally, we observed that of the two latent variables identified for social opinion, one of them, pessimism, was maintained while the other, optimism, was subdivided into optimism and expectation. [ABSTRACT FROM AUTHOR]