The voice of Twitter: observable subjective well-being inferred

As one of the major platforms of communication, social networks have become a valuable source of opinions and emotions. Considering that sharing of emotions offline and online is quite similar, historical posts from social networks seem to be a valuable source of data for measuring observable subjective well-being (OSWB). In this study, we calculated OSWB indices for the Russian-speaking segment of Twitter using the Affective Social Data Model for Socio-Technical Interactions. This model utilises demographic information and post-stratification techniques to make the data sample representative, by selected characteristics, of the general population of a country. For sentiment analysis, we fine-tuned RuRoBERTa-Large on RuSentiTweet and achieved new state-of-the-art results of F1 = 0.7229. Several calculated OSWB indicators demonstrated moderate Spearman’s correlation with the traditional survey-based net affect (rs = 0.469 and rs = 0.5332, p < 0.05) and positive affect (rs = 0.5177 and rs = 0.548, p < 0.05) indices in Russia.

(PDF) Gendered discourses and discursive strategies employed in Twitter-hashtagged debates about Saudi-women's issues

The promise of a model-based psychiatry: building computational models of mental ill health - The Lancet Digital Health

Factor Analysis

Frontiers Pre-suasive and persuasive strategies in the tweets of the Saudi Ministry of Health during the 2020 coronavirus pandemic: A corpus linguistic exploration

The promise of a model-based psychiatry: building computational models of mental ill health - The Lancet Digital Health

Amplifying influence through coordinated behaviour in social networks

The voice of Twitter: observable subjective well-being inferred from tweets in Russian

How weather impacts expressed sentiment in Russia: evidence from Odnoklassniki [PeerJ]

WHR 23, PDF, Happiness

Improving Sentiment Classification Performance through Coaching Architectures

A Multi-Brain Framework for Social Interaction: Trends in Neurosciences