Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media
Chan Young Park, Julia Mendelsohn, Anjalie Field, Yulia Tsvetkov
Abstract
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.
BibTeX
@inproceedings{park-etal-2022-challenges,
title = "Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime {R}ussian Media",
author = "Park, Chan Young and
Mendelsohn, Julia and
Field, Anjalie and
Tsvetkov, Yulia",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.382/",
doi = "10.18653/v1/2022.findings-emnlp.382",
pages = "5209--5235"
}