# Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit

arXiv: https://arxiv.org/abs/2205.14484 · HTML: https://mlsanity.com/paper/2205.14484/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 28 May 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach](https://mlsanity.com/paper/2104.01767/) (2021)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [Characterizing the 2016 Russian IRA Influence Campaign](https://mlsanity.com/paper/1812.01997/) (2018)
- [Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach](https://mlsanity.com/paper/1706.06314/) (2017)
- [No Calm in The Storm: Investigating QAnon Website Relationships](https://mlsanity.com/paper/2106.15715/) (2021)
- [Analysing the Extent of Misinformation in Cancer Related Tweets](https://mlsanity.com/paper/2003.13657/) (2020)

## Built on by (most cited first, 11 of 11)

- [Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale](https://mlsanity.com/paper/2308.02068/) (2023)
- [Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram](https://mlsanity.com/paper/2301.10856/) (2023)
- [Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study](https://mlsanity.com/paper/2310.14032/) (2023)
- [TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings](https://mlsanity.com/paper/2310.14450/) (2023)
- [Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings](https://mlsanity.com/paper/2506.00277/) (2025)
- [Anecdoctoring: Automated Red-Teaming Across Language and Place](https://mlsanity.com/paper/2509.19143/) (2025)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War](https://mlsanity.com/paper/2508.03599/) (2025)
- [Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War](https://mlsanity.com/paper/2409.07684/) (2024)
- [Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models](https://mlsanity.com/paper/2505.02858/) (2025)
- [LLM-based Detection of Manipulative Political Narratives](https://mlsanity.com/paper/2605.14354/) (2026)

## Abstract (arXiv)

In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian state media outlets output torrents of misleading and outright false information. In this work, we study this coordinated information campaign in order to understand the most prominent state media narratives touted by the Russian government to English-speaking audiences. To do this, we first perform sentence-level topic analysis using the large-language model MPNet on articles published by ten different pro-Russian propaganda websites including the new Russian "fact-checking" website waronfakes.com. Within this ecosystem, we show that smaller websites like katehon.com were highly effective at publishing topics that were later echoed by other Russian sites. After analyzing this set of Russian information narratives, we then analyze their correspondence with narratives and topics of discussion on the r/Russia and 10 other political subreddits. Using MPNet and a semantic search algorithm, we map these subreddits' comments to the set of topics extracted from our set of Russian websites, finding that 39.6% of r/Russia comments corresponded to narratives from pro-Russian propaganda websites compared to 8.86% on r/politics.