Smetanin Sergey, Komarov Mikhail
HSE University, Moscow, Russia.
PeerJ Comput Sci. 2022 Dec 20;8:e1181. doi: 10.7717/peerj-cs.1181. eCollection 2022.
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 = 0.7229. Several calculated OSWB indicators demonstrated moderate Spearman's correlation with the traditional survey-based net affect ( = 0.469 and = 0.5332, < 0.05) and positive affect ( = 0.5177 and = 0.548, < 0.05) indices in Russia.
作为主要的交流平台之一，社交网络已成为观点和情绪的宝贵来源。鉴于线下和线上的情绪分享颇为相似，社交网络上的历史帖子似乎是衡量可观察主观幸福感（OSWB）的宝贵数据来源。在本研究中，我们使用社会技术交互的情感社交数据模型计算了推特俄语区的OSWB指数。该模型利用人口统计信息和后分层技术，通过选定的特征使数据样本能够代表一个国家的普通人群。对于情感分析，我们在RuSentiTweet上对RuRoBERTa-Large进行了微调，并取得了新的最优结果，F1值 = 0.7229。在俄罗斯，几个计算得出的OSWB指标与传统的基于调查的净情感指数（rs = 0.469和rs = 0.5332，p < 0.05）以及积极情感指数（rs = 0.5177和rs = 0.548，p < 0.05）呈现出中等程度的斯皮尔曼相关性。
