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| Author | Xukun Li; Doina Caragea | ||||
| Title | Improving Disaster-related Tweet Classification with a Multimodal Approach | Type | Conference Article | ||
| Year | 2020 | Publication | ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2020 |
| Volume | Issue | Pages | 893-902 | ||
| Keywords | Multimodal Model; Tweet Classification; Deep Learning | ||||
| Abstract | Social media data analysis is important for disaster management. Lots of prior studies have focused on classifying a tweet based on its text or based on its images, independently, even if the tweet contains both text and images. Under the assumptions that text and images may contain complementary information, it is of interest to construct classifiers that make use of both modalities of the tweet. Towards this goal, we propose a multimodal classification model which aggregates text and image information. Our study aims to provide insights into the benefits obtained by combining text and images, and to understand what type of modality is more informative with respect to disaster tweet classification. Experimental results show that both text and image classification can be improved by the multimodal approach. | ||||
| Address | Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-27-79 | ISBN | 2411-3465 | Medium | |
| Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
| Notes | xukun@ksu.edu | Approved | no | ||
| Call Number | Serial | 2280 | |||
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