# Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

arXiv: https://arxiv.org/abs/2205.12382 · HTML: https://mlsanity.com/paper/2205.12382/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 94 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [A Survey on Computational Propaganda Detection](https://mlsanity.com/paper/2007.08024/) (2020)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- ["You are grounded!": Latent Name Artifacts in Pre-trained Language Models](https://mlsanity.com/paper/2004.03012/) (2020)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017](https://mlsanity.com/paper/2005.01803/) (2020)
- [FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding](https://mlsanity.com/paper/2002.08608/) (2020)
- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (2021)
- [Entity-Centric Contextual Affective Analysis](https://mlsanity.com/paper/1906.01762/) (2019)
- [Twitter Dataset for 2022 Russo-Ukrainian Crisis](https://mlsanity.com/paper/2203.02955/) (2022)
- [Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia](https://mlsanity.com/paper/2203.07488/) (2022)

## Built on by (most cited first, 10 of 10)

- [Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram](https://mlsanity.com/paper/2301.10856/) (2023)
- [Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments](https://mlsanity.com/paper/2507.04612/) (2025)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation](https://mlsanity.com/paper/2402.03642/) (2024)
- [Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection](https://mlsanity.com/paper/2603.01993/) (2026)
- [Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflict](https://mlsanity.com/paper/2306.12886/) (2023)
- [OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War](https://mlsanity.com/paper/2508.03599/) (2025)
- [When a Language Question Is at Stake. A Revisited Approach to Label Sensitive Content](https://mlsanity.com/paper/2311.10514/) (2023)
- [Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns](https://mlsanity.com/paper/2408.13651/) (2024)
- [A Longitudinal, Multinational, and Multilingual Corpus of News Coverage of the Russo-Ukrainian War](https://mlsanity.com/paper/2601.16309/) (2026)

## Abstract (arXiv)

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.