# Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram

arXiv: https://arxiv.org/abs/2301.10856 · HTML: https://mlsanity.com/paper/2301.10856/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 73 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign](https://mlsanity.com/paper/1802.04291/) (2018)
- [On the Globalization of the QAnon Conspiracy Theory Through Telegram](https://mlsanity.com/paper/2105.13020/) (2021)
- [The Pushshift Telegram Dataset](https://mlsanity.com/paper/2001.08438/) (2020)
- [Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit](https://mlsanity.com/paper/2205.14484/) (2022)
- [Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media](https://mlsanity.com/paper/2205.12382/) (2022)
- [Scalable Bayesian Inference for Excitatory Point Process Networks](https://mlsanity.com/paper/1507.03228/) (2015)
- [Overcoming Language Disparity in Online Content Classification with Multimodal Learning](https://mlsanity.com/paper/2205.09744/) (2022)
- [No Calm in The Storm: Investigating QAnon Website Relationships](https://mlsanity.com/paper/2106.15715/) (2021)
- [Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements](https://mlsanity.com/paper/2111.13530/) (2021)
- ["A Special Operation": A Quantitative Approach to Dissecting and Comparing Different Media Ecosystems' Coverage of the Russo-Ukrainian War](https://mlsanity.com/paper/2210.03016/) (2022)
- [Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI](https://mlsanity.com/paper/2208.04608/) (2022)

## Built on by (most cited first, 5 of 5)

- [Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites](https://mlsanity.com/paper/2305.09820/) (2023)
- [Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale](https://mlsanity.com/paper/2308.02068/) (2023)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War](https://mlsanity.com/paper/2409.07684/) (2024)
- [Unifying Models of Intergroup Hostility in Online Discourse](https://mlsanity.com/paper/2609.20808/) (2026)

## Abstract (arXiv)

In response to disinformation and propaganda from Russian online media following the invasion of Ukraine, Russian media outlets such as Russia Today and Sputnik News were banned throughout Europe. To maintain viewership, many of these Russian outlets began to heavily promote their content on messaging services like Telegram. In this work, we study how 16 Russian media outlets interacted with and utilized 732 Telegram channels throughout 2022. Leveraging the foundational model MPNet, DP-means clustering, and Hawkes processes, we trace how narratives spread between news sites and Telegram channels. We show that news outlets not only propagate existing narratives through Telegram but that they source material from the messaging platform. For example, across the websites in our study, between 2.3% (ura.news) and 26.7% (ukraina.ru) of articles discussed content that originated/resulted from activity on Telegram. Finally, tracking the spread of individual topics, we measure the rate at which news outlets and Telegram channels disseminate content within the Russian media ecosystem, finding that websites like ura.news and Telegram channels such as @genshab are the most effective at disseminating their content.