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Collecting public opinion data is challenging in the shadow of war. And yet accurate public opinion is crucial. Political elites rely on it and often attempt to influence it. Therefore, it is incumbent on researchers to provide independent and reliable wartime polls. However, surveying in wartime presents a distinctive set of challenges. We outline two challenges facing polling in war: under-coverage and response bias. We highlight these challenges in the context of the Russia– Ukraine war, drawing on original panel survey data tracing the attitudes of the same people prior to and after Russia’s full-scale invasion of Ukraine in 2022. We conclude with some lessons for those employing survey methods in wartime, and point to steps forward, in Ukraine and beyond.
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Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media
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.
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- Award ID(s):
- 2142739
- PAR ID:
- 10433105
- Date Published:
- Journal Name:
- Empirical Methods in Natural Language Processing
- Page Range / eLocation ID:
- 5209–5235
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
