  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.
