@inproceedings{xu-etal-2023-leveraging,
title = "Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection",
author = "Xu, Fan and
Fu, Pinyun and
Huang, Qi and
Zou, Bowei and
Aw, AiTi and
Wang, Mingwen",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.900",
doi = "10.18653/v1/2023.findings-emnlp.900",
pages = "13492--13503",
abstract = "Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as well as the challenges posed by incomplete modalities in single modal posts, such as missing text or images. This paper presents CLKD-IMRD, a novel framework for Incomplete Modality Rumor Detection. CLKD-IMRD employs Contrastive Learning and Knowledge Distillation to capture the semantic consistency between text and image pairs, while also enhancing model generalization to incomplete modalities within individual posts. Extensive experimental results demonstrate that our CLKD-IMRD outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media.",
}
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<abstract>Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as well as the challenges posed by incomplete modalities in single modal posts, such as missing text or images. This paper presents CLKD-IMRD, a novel framework for Incomplete Modality Rumor Detection. CLKD-IMRD employs Contrastive Learning and Knowledge Distillation to capture the semantic consistency between text and image pairs, while also enhancing model generalization to incomplete modalities within individual posts. Extensive experimental results demonstrate that our CLKD-IMRD outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media.</abstract>
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%0 Conference Proceedings
%T Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection
%A Xu, Fan
%A Fu, Pinyun
%A Huang, Qi
%A Zou, Bowei
%A Aw, AiTi
%A Wang, Mingwen
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F xu-etal-2023-leveraging
%X Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as well as the challenges posed by incomplete modalities in single modal posts, such as missing text or images. This paper presents CLKD-IMRD, a novel framework for Incomplete Modality Rumor Detection. CLKD-IMRD employs Contrastive Learning and Knowledge Distillation to capture the semantic consistency between text and image pairs, while also enhancing model generalization to incomplete modalities within individual posts. Extensive experimental results demonstrate that our CLKD-IMRD outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media.
%R 10.18653/v1/2023.findings-emnlp.900
%U https://aclanthology.org/2023.findings-emnlp.900
%U https://doi.org/10.18653/v1/2023.findings-emnlp.900
%P 13492-13503
Markdown (Informal)
[Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection](https://aclanthology.org/2023.findings-emnlp.900) (Xu et al., Findings 2023)
ACL