Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ICMR ´23 |
| Untertitel | Proceedings of the 2023 ACM International Conference on Multimedia Retrieval |
| Seiten | 581-585 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9798400701788 |
| Publikationsstatus | Veröffentlicht - 12 Juni 2023 |
| Veranstaltung | 2023 ACM International Conference on Multimedia Retrieval, ICMR 2023 - Thessaloniki, Griechenland Dauer: 12 Juni 2023 → 15 Juni 2023 |
Abstract
The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are not applicable to real-world data. In this paper, we propose three models that adopt and fine-tune state-of-the-art multimodal transformers for multimodal fake news detection. We conduct an in-depth analysis by manipulating the input data aimed to explore models performance in realistic use cases on social media. Our study across multiple models demonstrates that these systems suffer significant performance drops against manipulated data. To reduce the bias and improve model generalization, we suggest training data augmentation to conduct more meaningful experiments for fake news detection on social media. The proposed data augmentation techniques enable models to generalize better and yield improved state-of-the-art results.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Software
- Informatik (insg.)
- Computergrafik und computergestütztes Design
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ICMR ´23 : Proceedings of the 2023 ACM International Conference on Multimedia Retrieval. 2023. S. 581-585.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Improving Generalization for Multimodal Fake News Detection
AU - Tahmasebi, Sahar
AU - Hakimov, Sherzod
AU - Ewerth, Ralph
AU - Müller-Budack, Eric
N1 - Funding Information: This work was funded by European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 812997 (CLEOPATRA ITN), and by the German Federal Ministry of Education and Research (BMBF, FakeNarratives project, no. 16KIS1517).
PY - 2023/6/12
Y1 - 2023/6/12
N2 - The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are not applicable to real-world data. In this paper, we propose three models that adopt and fine-tune state-of-the-art multimodal transformers for multimodal fake news detection. We conduct an in-depth analysis by manipulating the input data aimed to explore models performance in realistic use cases on social media. Our study across multiple models demonstrates that these systems suffer significant performance drops against manipulated data. To reduce the bias and improve model generalization, we suggest training data augmentation to conduct more meaningful experiments for fake news detection on social media. The proposed data augmentation techniques enable models to generalize better and yield improved state-of-the-art results.
AB - The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are not applicable to real-world data. In this paper, we propose three models that adopt and fine-tune state-of-the-art multimodal transformers for multimodal fake news detection. We conduct an in-depth analysis by manipulating the input data aimed to explore models performance in realistic use cases on social media. Our study across multiple models demonstrates that these systems suffer significant performance drops against manipulated data. To reduce the bias and improve model generalization, we suggest training data augmentation to conduct more meaningful experiments for fake news detection on social media. The proposed data augmentation techniques enable models to generalize better and yield improved state-of-the-art results.
KW - Multimodal fake news detection
KW - news analytics
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=85163636675&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2305.18599
DO - 10.48550/arXiv.2305.18599
M3 - Conference contribution
AN - SCOPUS:85163636675
SP - 581
EP - 585
BT - ICMR ´23
T2 - 2023 ACM International Conference on Multimedia Retrieval, ICMR 2023
Y2 - 12 June 2023 through 15 June 2023
ER -