| Titre : |
FactCheckBureau : build your own fact-check analysis pipeline |
| Type de document : |
document électronique |
| Auteurs : |
Brahim Saadi, Auteur ; Mohammed Younes Elfraihi, Auteur ; Iskander Zouaghi, Directeur de thèse ; Manolescu Ioana, Directeur de thèse ; Balalau Oana, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2024 |
| Importance : |
1 fichier PDF (2.7 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet
Mémoire de Projet de Fin d’Etudes : Génie Industriel. Data Science-Intelligence Artificielle : Alger, Ecole Nationale Polytechnique : 2024
Bibliogr. p. 106 - 107 . - Annexe p. 108 - 113 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Fact-checking Misinformation Automated systems Tweet-article matching Information retrieval BM25 SBERT Sentence-level similarity Tweet enrichment English and French FactCheckBureau |
| Index. décimale : |
PI02624 |
| Résumé : |
This thesis explores the development and evaluation of automated fact-checking systems, focusing on matching claims and tweets to fact-checking articles. We assess retrieval and re-ranking methods, such as the BM25 algorithm and SBERT model. Key contributions include:
• Sentence-Level Similarity: A novel approach for SBERT re-ranking improves accuracy in tweet-article matching.
• Language-Specific Analysis: Comparative analysis of English and French claims highlights the need for language-specific models.
• FactCheckBureau Platform: A web application designed to help researchers and journalists develop accurate claim-fact check matching systems.
Our experiments reveal the strengths and limitations of various methods. While BM25 serves as a robust baseline, SBERT with sentence-level granularity enhances precision. We also explore tweet enrichment techniques like OCR and image captioning to improve tweet representation. This research advances automated fact-checking, offering tools and insights to combat misinformation. The FactCheckBureau platform enables effective claim verification, promoting accurate information online. |
FactCheckBureau : build your own fact-check analysis pipeline [document électronique] / Brahim Saadi, Auteur ; Mohammed Younes Elfraihi, Auteur ; Iskander Zouaghi, Directeur de thèse ; Manolescu Ioana, Directeur de thèse ; Balalau Oana, Directeur de thèse . - [S.l.] : [s.n.], 2024 . - 1 fichier PDF (2.7 Mo) : ill. Mode d'accès : accès au texte intégral par intranet
Mémoire de Projet de Fin d’Etudes : Génie Industriel. Data Science-Intelligence Artificielle : Alger, Ecole Nationale Polytechnique : 2024
Bibliogr. p. 106 - 107 . - Annexe p. 108 - 113 Langues : Anglais ( eng)
| Mots-clés : |
Fact-checking Misinformation Automated systems Tweet-article matching Information retrieval BM25 SBERT Sentence-level similarity Tweet enrichment English and French FactCheckBureau |
| Index. décimale : |
PI02624 |
| Résumé : |
This thesis explores the development and evaluation of automated fact-checking systems, focusing on matching claims and tweets to fact-checking articles. We assess retrieval and re-ranking methods, such as the BM25 algorithm and SBERT model. Key contributions include:
• Sentence-Level Similarity: A novel approach for SBERT re-ranking improves accuracy in tweet-article matching.
• Language-Specific Analysis: Comparative analysis of English and French claims highlights the need for language-specific models.
• FactCheckBureau Platform: A web application designed to help researchers and journalists develop accurate claim-fact check matching systems.
Our experiments reveal the strengths and limitations of various methods. While BM25 serves as a robust baseline, SBERT with sentence-level granularity enhances precision. We also explore tweet enrichment techniques like OCR and image captioning to improve tweet representation. This research advances automated fact-checking, offering tools and insights to combat misinformation. The FactCheckBureau platform enables effective claim verification, promoting accurate information online. |
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PI00100 
