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.

Psi Chi Journal of Psychological Research - Winter 2023 by Psi Chi, the International Honor Society in Psychology - Issuu

Amplifying influence through coordinated behaviour in social networks

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

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

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

Influencing human–AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness

Circadian mood variations in Twitter content - Fabon Dzogang, Stafford Lightman, Nello Cristianini, 2017

Circadian mood variations in Twitter content - Fabon Dzogang, Stafford Lightman, Nello Cristianini, 2017