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    <abstract>Social media platforms have enabled large-scale influence campaigns, impacting democratic processes. To fight against these threats, continuous training is needed. A typical training session is based on a fictive scenario describing key elements which are instantiated into a dedicated platform.Such a platform simulates social networks, which host a huge amount of content aligned with the training scenario. However, directly using Large Language Models to create appropriate content result in low content diversity due to coarse-grained and high-level scenario constraints, which compromises the trainees’ immersion.We address this issue with SocialForge, a system designed toenhance the diversity and realism of the generated content while ensuring its adherence to the original scenario.Specifically, SocialForge refines and augments the initial scenario constraints by generating detailed subnarratives, personas, and events.We assess diversity, realism, and adherence to the scenario through custom evaluation protocol. We also propose an automatic method to detect erroneous constraint generation, ensuring optimal alignment of the content with the scenario.SocialForge has been used in real trainings and in several showcases, with great end-user satisfaction. We release an open-source dataset generated with SocialForge for the research community.</abstract>
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%0 Conference Proceedings
%T SocialForge: simulating the social internet to provide realistic training against influence operations
%A Oliveri, Ulysse
%A Gadek, Guillaume
%A Dey, Alexandre
%A Costé, Benjamin
%A Lolive, Damien
%A Delhay, Arnaud
%A Grilheres, Bruno
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F oliveri-etal-2025-socialforge
%X Social media platforms have enabled large-scale influence campaigns, impacting democratic processes. To fight against these threats, continuous training is needed. A typical training session is based on a fictive scenario describing key elements which are instantiated into a dedicated platform.Such a platform simulates social networks, which host a huge amount of content aligned with the training scenario. However, directly using Large Language Models to create appropriate content result in low content diversity due to coarse-grained and high-level scenario constraints, which compromises the trainees’ immersion.We address this issue with SocialForge, a system designed toenhance the diversity and realism of the generated content while ensuring its adherence to the original scenario.Specifically, SocialForge refines and augments the initial scenario constraints by generating detailed subnarratives, personas, and events.We assess diversity, realism, and adherence to the scenario through custom evaluation protocol. We also propose an automatic method to detect erroneous constraint generation, ensuring optimal alignment of the content with the scenario.SocialForge has been used in real trainings and in several showcases, with great end-user satisfaction. We release an open-source dataset generated with SocialForge for the research community.
%R 10.18653/v1/2025.acl-industry.13
%U https://aclanthology.org/2025.acl-industry.13/
%U https://doi.org/10.18653/v1/2025.acl-industry.13
%P 166-178

Markdown (Informal)

[SocialForge: simulating the social internet to provide realistic training against influence operations](https://aclanthology.org/2025.acl-industry.13/) (Oliveri et al., ACL 2025)

• SocialForge: simulating the social internet to provide realistic training against influence operations (Oliveri et al., ACL 2025)

ACL

• Ulysse Oliveri, Guillaume Gadek, Alexandre Dey, Benjamin Costé, Damien Lolive, Arnaud Delhay, and Bruno Grilheres. 2025. SocialForge: simulating the social internet to provide realistic training against influence operations. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 166–178, Vienna, Austria. Association for Computational Linguistics.