Abstract
Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause. In this work, we model the task of peti-tion popularity using both text and image rep-resentations across four different languages,and including petition metadata. We evaluateour proposed approach using a dataset of 75kpetitions from Avaaz.org, and find strong com-plementarity between text and images.- Anthology ID:
- 2020.alta-1.14
- Volume:
- Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
- Month:
- December
- Year:
- 2020
- Address:
- Virtual Workshop
- Editors:
- Maria Kim, Daniel Beck, Meladel Mistica
- Venue:
- ALTA
- SIG:
- Publisher:
- Australasian Language Technology Association
- Note:
- Pages:
- 110–114
- Language:
- URL:
- https://aclanthology.org/2020.alta-1.14
- DOI:
- Cite (ACL):
- Kotaro Kitayama, Shivashankar Subramanian, and Timothy Baldwin. 2020. Popularity Prediction of Online Petitions using a Multimodal DeepRegression Model. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 110–114, Virtual Workshop. Australasian Language Technology Association.
- Cite (Informal):
- Popularity Prediction of Online Petitions using a Multimodal DeepRegression Model (Kitayama et al., ALTA 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.alta-1.14.pdf