@inproceedings{kitayama-etal-2020-popularity,
    title = "Popularity Prediction of Online Petitions using a Multimodal {D}eep{R}egression Model",
    author = "Kitayama, Kotaro  and
      Subramanian, Shivashankar  and
      Baldwin, Timothy",
    editor = "Kim, Maria  and
      Beck, Daniel  and
      Mistica, Meladel",
    booktitle = "Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.alta-1.14/",
    pages = "110--114",
    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."
}