@inproceedings{mehta-goldwasser-2021-tackling,
    title = "Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks",
    author = "Mehta, Nikhil  and
      Goldwasser, Dan",
    editor = {Brantley, Kiant{\'e}  and
      Dan, Soham  and
      Gurevych, Iryna  and
      Lee, Ji-Ung  and
      Radlinski, Filip  and
      Sch{\"u}tze, Hinrich  and
      Simpson, Edwin  and
      Yu, Lili},
    booktitle = "Proceedings of the First Workshop on Interactive Learning for Natural Language Processing",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.internlp-1.7/",
    doi = "10.18653/v1/2021.internlp-1.7",
    pages = "46--53",
    abstract = "Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in today{'}s society. However, this has also enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs. Detecting fake news is important to prevent misinformation and maintain a healthy society. While prior works have tackled this problem by building supervised learning systems, automatedly modeling the social media landscape that enables the spread of fake news is challenging. On the contrary, having humans fact check all news is not scalable. Thus, in this paper, we propose to approach this problem \textit{interactively}, where human insight can be continually combined with an automated system, enabling better social media representation quality. Our experiments show performance improvements in this setting."
}