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In this paper, we design a cross-modal attention fusion network with orthogonal latent memory (CALM) to fuse multi-modal social media data for rumor detection. Given multimodal content features extracted from text and images, we devise a cross-modal attention fusion (CAF) mechanism to extract critical information underlying the modalities by intra-modality attention, and model the underlying relations among the modalities by inter-modality attention. In terms of the text, the natural sequential characteristics are critical to semantic understanding, while existing sequence models suffer from losing the information conveyed by the former words. To this end, we propose a Bi-GRU with orthogonal latent memory to extract the sequential features from the text, where the memory captures independent patterns. The fused content features and the sequential features can be used for rumor detection seamlessly. Extensive experiments conducted on two real-world datasets show the outperformance of the proposed CALM. (e.g., F1 -score is improved from 0.823 to 0.846 on Weibo dataset).
|Title of host publication||Web Information Systems Engineering – WISE 2021|
|Subtitle of host publication||22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26–29, 2021, Proceedings, Part I|
|Editors||Wenjie ZHANG, Lei ZOU, Zakaria MAAMAR, Lu CHEN|
|Number of pages||15|
|Publication status||Published - Jan 2022|
|Event||22nd International Conference on Web Information Systems Engineering - Melbourne, Australia|
Duration: 26 Oct 2021 → 29 Oct 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||22nd International Conference on Web Information Systems Engineering|
|Abbreviated title||WISE 2021|
|Period||26/10/21 → 29/10/21|
Bibliographical noteFunding Information:
Acknowledgement. This work is supported by the National Natural Science Foundation of China (No. 62076073), the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010616), Science and Technology Program of Guangzhou (No. 202102020524), the Guangdong Innovative Research Team Program (No.2014Z T05G157), HKIBS Research Program Grant Application (HCRG-201-002) and the Faculty Research Grant (DB21B6) of Lingnan University, Hong Kong.
© 2021, Springer Nature Switzerland AG.
- Rumor detection
- Social media
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- 1 Finished
A Label Extension Schema for Improved Text Emotion Classification
1/07/21 → 30/06/22
Project: Grant Research