Russia's attack on Ukraine on Thursday 24 February 2022 hitched financial
markets and the increased geopolitical crisis. In this paper, we select some
main economic indexes, such as Gold, Oil (WTI), NDAQ, and known currency which
are involved in this crisis and try to find the quantitative effect of this war
on them. To quantify the war effect, we use the correlation feature and the
relationships between these economic indices, create datasets, and compare the
results of forecasts with real data. To study war effects, we use Machine
Learning Linear Regression. We carry on empirical experiments and perform on
these economic indices datasets to evaluate and predict this war tolls and its
effects on main economics indexes.

本文研究了俄罗斯对乌克兰的战争对金融市场的影响，选取了金、原油、股票指数和货币等主要经济指标，通过机器学习线性回归的方法，分析战争对这些指标的实际和预期影响。

应用机器学习模型预测乌克兰危机的影响

Machine learning model to project the impact of Ukraine crisis

There is evidence that Russia's Internet Research Agency attempted to
interfere with the 2016 U.S. election by running fake accounts on Twitter -
often referred to as "Russian trolls". In this work, we: 1) develop machine
learning models that predict whether a Twitter account is a Russian troll
within a set of 170K control accounts; and, 2) demonstrate that it is possible
to use this model to find active accounts on Twitter still likely acting on
behalf of the Russian state. Using both behavioral and linguistic features, we
show that it is possible to distinguish between a troll and a non-troll with a
precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the
model to out-of-sample accounts still active today, we find that up to 2.6% of
top journalists' mentions are occupied by Russian trolls. These findings imply
that the Russian trolls are very likely still active today. Additional analysis
shows that they are not merely software-controlled bots, and manage their
online identities in various complex ways. Finally, we argue that if it is
possible to discover these accounts using externally - accessible data, then
the platforms - with access to a variety of private internal signals - should
succeed at similar or better rates.

本文使用机器学习模型，通过行为和语言特征来区分俄罗斯网络特工与非特工的 Twitter 账户，表明拥有各种内部信号的平台应该可以发现更多俄罗斯特工账户。

仍在存在：对推特上的俄罗斯 “喷子” 账户建模和识别

Still out there: Modeling and Identifying Russian Troll Accounts on  Twitter

Amidst growing concern over media manipulation, NLP attention has focused on
overt strategies like censorship and "fake news'". Here, we draw on two
concepts from the political science literature to explore subtler strategies
for government media manipulation: agenda-setting (selecting what topics to
cover) and framing (deciding how topics are covered). We analyze 13 years (100K
articles) of the Russian newspaper Izvestia and identify a strategy of
distraction: articles mention the U.S. more frequently in the month directly
following an economic downturn in Russia. We introduce embedding-based methods
for cross-lingually projecting English frames to Russian, and discover that
these articles emphasize U.S. moral failings and threats to the U.S. Our work
offers new ways to identify subtle media manipulation strategies at the
intersection of agenda-setting and framing.

研究俄罗斯 Izvestia 报纸 13 年 (10 万篇文章) 的数据，发现文章在俄罗斯经济衰退后一个月内更多提及美国，呈现出有目的的偏向性，文章还探讨了策略性的议程设置和框架决策对媒体操纵的影响。