Papers by Guillaume Gadek
SocialForge: simulating the social internet to provide realistic training against influence operations (2025.acl-industry)
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Ulysse Oliveri, Guillaume Gadek, Alexandre Dey, Benjamin Costé, Damien Lolive, Arnaud Delhay, Bruno Grilheres
| Challenge: | Social media platforms have enabled large-scale influence campaigns, impacting democratic processes. |
| Approach: | They propose a system to enhance diversity and realism of the generated content while ensuring its adherence to the original scenario. |
| Outcome: | The proposed system improves diversity and realism while ensuring its adherence to the original scenario. |
K-pop and fake facts: from texts to smart alerting for maritime security (2023.acl-industry)
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Maxime Prieur, Souhir Gahbiche, Guillaume Gadek, Sylvain Gatepaille, Kilian Vasnier, Valerian Justine
| Challenge: | Maritime security requires full-time monitoring of the situation based on technical data but also from OSINT-like inputs. |
| Approach: | They propose a system that extracts data from sensors and texts to feed a Knowledge Base . the system can be used to detect malicious actors using AIS and pseudo-newspapers . |
| Outcome: | The proposed system ingests data from sensors and texts and feeds a Knowledge Base . it performs coherence checks between extracted facts and the extracted data . |
Arabizi Language Models for Sentiment Analysis (2020.coling-main)
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| Challenge: | Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. |
| Approach: | They propose to use Arabizi as a written form of spoken Arabic in online social networks . they use a corpus of 7.7M tweets written in Arabizi and a subset of SALAD to train a model in Arabic . |
| Outcome: | The proposed model outperforms state-of-the-art models on sentiment analysis task using arabizi . the proposed model is based on a corpus of 7.7M tweets written in arabizi and a subset of LAD manually annotated for sentiment analysis. |
A Multi-Label Dataset of French Fake News: Human and Machine Insights (2024.lrec-main)
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Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye, Julien Chanson, Guillaume Gadek, Ghislain Atemezing, François Bancilhon, Paul Égré
| Challenge: | a corpus of documents selected from 17 sources of french press considered unreliable by experts is annotated using 11 labels by 8 annotators. |
| Approach: | They present a corpus of documents annotated using 11 labels by 8 annotators . they use a subjectivity analyzer VAGO to clarify the link between subjective and fake news labels . |
| Outcome: | The proposed dataset identifies features that humans consider as characteristic of fake news . it also clarifies the link between ascriptions of the label Subjective and ascribeds of Fake News . |