With Twitter's growth and popularity, a huge number of views are shared by
users on various topics, making this platform a valuable information source on
various political, social, and economic issues. This paper investigates English
tweets on the Russia-Ukraine war to analyze trends reflecting users' opinions
and sentiments regarding the conflict. The tweets' positive and negative
sentiments are analyzed using a BERT-based model, and the time series
associated with the frequency of positive and negative tweets for various
countries is calculated. Then, we propose a method based on the neighborhood
average for modeling and clustering the time series of countries. The
clustering results provide valuable insight into public opinion regarding this
conflict. Among other things, we can mention the similar thoughts of users from
the United States, Canada, the United Kingdom, and most Western European
countries versus the shared views of Eastern European, Scandinavian, Asian, and
South American nations toward the conflict.

本研究使用基于 BERT 的模型分析推特上关于俄乌战争的观点和情感，采用邻域平均法模拟和聚类各国家时间序列，并提供有关公众对此冲突看法的有价值的见解。

俄乌战争：各国情感趋势建模与聚类

Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries

NLP research on public opinion manipulation campaigns has primarily focused
on detecting overt strategies such as fake news and disinformation. However,
information manipulation in the ongoing Russia-Ukraine war exemplifies how
governments and media also employ more nuanced strategies. We release a new
dataset, VoynaSlov, containing 38M+ posts from Russian media outlets on Twitter
and VKontakte, as well as public activity and responses, immediately preceding
and during the 2022 Russia-Ukraine war. We apply standard and
recently-developed NLP models on VoynaSlov to examine agenda setting, framing,
and priming, several strategies underlying information manipulation, and reveal
variation across media outlet control, social media platform, and time. Our
examination of these media effects and extensive discussion of current
approaches' limitations encourage further development of NLP models for
understanding information manipulation in emerging crises, as well as other
real-world and interdisciplinary tasks.

本研究主要通过分析俄罗斯媒体对于俄乌战争期间相关言论（VoynaSlov 数据集）的新闻报道和社交媒体营销信息，揭示了信息操纵的不同策略和媒体效应，研究结果为进一步发展用于应急危机和社科领域的 NLP 模型提供了参考。