@inproceedings{tonneau-etal-2026-swarm,
title = "{SWARM}: A Multilingual Human-Annotated Dataset for {R}ussian Propaganda Detection in Search Engine Results",
author = "Tonneau, Manuel and
Dubey, Abhinav and
Shaikh, Farhan and
Vitulano, Ilaria and
Stolze, Martha and
Dedeoglu, Hale and
Riechert, Clara and
Kuka, Ella and
Sydorova, Maryna and
Makhortykh, Mykola and
Kuznetsova, Elizaveta",
editor = "Calabrese, Agostina and
Davidson, Thomas and
de Kock, Christine and
Khurana, Urja and
Marchiori Manerba, Marta and
Piot, Paloma and
Talat, Zeerak",
booktitle = "Proceedings of the 10th Workshop on Online Abuse and Harms ({WOAH})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.woah-1.33/",
pages = "374--391",
ISBN = "979-8-89176-487-3",
abstract = "Russian state propaganda spreads across many languages and online spaces. Yet, most computational work examines only one such space, usually social media, in one or two languages, and analyses sources rather than content. We introduce SWARM (Search-Web documents Annotated for Russian propaganda, Multilingual), a dataset of 2,183 search engine results across nine languages and diverse web domains (e.g., news, blogs, government sites), each annotated by trained coders for whether it supports a recurring Russian propaganda narrative. We benchmark a source-based blocklist, supervised classifiers, and zero-shot LLMs against these labels. The blocklist misses most propaganda-supporting documents, because such content is not confined to flagged ``propaganda'' outlets but also appears on mainstream ones. Content-level analysis helps, though how much depends on the model: the strongest LLM reaches a positive-class F1 of 0.73, whereas the supervised classifiers reach only about 0.5, with the smaller LLMs over-predicting support, mistaking topical relevance for endorsement. Detecting search-borne propaganda thus requires per-language, content-level evaluation, which we hope SWARM and our evaluation code enable."
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<abstract>Russian state propaganda spreads across many languages and online spaces. Yet, most computational work examines only one such space, usually social media, in one or two languages, and analyses sources rather than content. We introduce SWARM (Search-Web documents Annotated for Russian propaganda, Multilingual), a dataset of 2,183 search engine results across nine languages and diverse web domains (e.g., news, blogs, government sites), each annotated by trained coders for whether it supports a recurring Russian propaganda narrative. We benchmark a source-based blocklist, supervised classifiers, and zero-shot LLMs against these labels. The blocklist misses most propaganda-supporting documents, because such content is not confined to flagged “propaganda” outlets but also appears on mainstream ones. Content-level analysis helps, though how much depends on the model: the strongest LLM reaches a positive-class F1 of 0.73, whereas the supervised classifiers reach only about 0.5, with the smaller LLMs over-predicting support, mistaking topical relevance for endorsement. Detecting search-borne propaganda thus requires per-language, content-level evaluation, which we hope SWARM and our evaluation code enable.</abstract>
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%0 Conference Proceedings
%T SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results
%A Tonneau, Manuel
%A Dubey, Abhinav
%A Shaikh, Farhan
%A Vitulano, Ilaria
%A Stolze, Martha
%A Dedeoglu, Hale
%A Riechert, Clara
%A Kuka, Ella
%A Sydorova, Maryna
%A Makhortykh, Mykola
%A Kuznetsova, Elizaveta
%Y Calabrese, Agostina
%Y Davidson, Thomas
%Y de Kock, Christine
%Y Khurana, Urja
%Y Marchiori Manerba, Marta
%Y Piot, Paloma
%Y Talat, Zeerak
%S Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-487-3
%F tonneau-etal-2026-swarm
%X Russian state propaganda spreads across many languages and online spaces. Yet, most computational work examines only one such space, usually social media, in one or two languages, and analyses sources rather than content. We introduce SWARM (Search-Web documents Annotated for Russian propaganda, Multilingual), a dataset of 2,183 search engine results across nine languages and diverse web domains (e.g., news, blogs, government sites), each annotated by trained coders for whether it supports a recurring Russian propaganda narrative. We benchmark a source-based blocklist, supervised classifiers, and zero-shot LLMs against these labels. The blocklist misses most propaganda-supporting documents, because such content is not confined to flagged “propaganda” outlets but also appears on mainstream ones. Content-level analysis helps, though how much depends on the model: the strongest LLM reaches a positive-class F1 of 0.73, whereas the supervised classifiers reach only about 0.5, with the smaller LLMs over-predicting support, mistaking topical relevance for endorsement. Detecting search-borne propaganda thus requires per-language, content-level evaluation, which we hope SWARM and our evaluation code enable.
%U https://aclanthology.org/2026.woah-1.33/
%P 374-391
Markdown (Informal)
[SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results](https://aclanthology.org/2026.woah-1.33/) (Tonneau et al., WOAH 2026)
ACL
- Manuel Tonneau, Abhinav Dubey, Farhan Shaikh, Ilaria Vitulano, Martha Stolze, Hale Dedeoglu, Clara Riechert, Ella Kuka, Maryna Sydorova, Mykola Makhortykh, and Elizaveta Kuznetsova. 2026. SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results. In Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH), pages 374–391, Budapest, Hungary. Association for Computational Linguistics.