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    <abstract>This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn.world) and WarOnFakes (waronfakes.com), which publish content in Arabic, Chinese, English, French, German, and Spanish. We describe our content acquisition methodology and perform cross-site unsupervised topic clustering on the resulting multilingual dataset. We also perform linguistic and temporal analysis of the web page translations and topics over time, and investigate articles with false publication dates. We make publicly available this new dataset of 14,053 articles, annotated with each language version, and additional metadata such as links and images. The main contribution of this paper for the NLP community is in the novel dataset which enables studies of disinformation networks, and the training of NLP tools for disinformation detection.</abstract>
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%0 Conference Proceedings
%T Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study
%A Heppell, Freddy
%A Bontcheva, Kalina
%A Scarton, Carolina
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F heppell-etal-2023-analysing
%X This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn.world) and WarOnFakes (waronfakes.com), which publish content in Arabic, Chinese, English, French, German, and Spanish. We describe our content acquisition methodology and perform cross-site unsupervised topic clustering on the resulting multilingual dataset. We also perform linguistic and temporal analysis of the web page translations and topics over time, and investigate articles with false publication dates. We make publicly available this new dataset of 14,053 articles, annotated with each language version, and additional metadata such as links and images. The main contribution of this paper for the NLP community is in the novel dataset which enables studies of disinformation networks, and the training of NLP tools for disinformation detection.
%R 10.18653/v1/2023.emnlp-main.349
%U https://aclanthology.org/2023.emnlp-main.349
%U https://doi.org/10.18653/v1/2023.emnlp-main.349
%P 5729-5741

Markdown (Informal)

[Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study](https://aclanthology.org/2023.emnlp-main.349) (Heppell et al., EMNLP 2023)

• Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study (Heppell et al., EMNLP 2023)

ACL

• Freddy Heppell, Kalina Bontcheva, and Carolina Scarton. 2023. Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5729–5741, Singapore. Association for Computational Linguistics.