@inproceedings{sakketou-etal-2022-temporal,
    title = "Temporal Graph Analysis of Misinformation Spreaders in Social Media",
    author = "Plepi, Joan  and
      Sakketou, Flora  and
      Geiss, Henri-Jacques  and
      Flek, Lucie",
    editor = "Ustalov, Dmitry  and
      Gao, Yanjun  and
      Panchenko, Alexander  and
      Valentino, Marco  and
      Thayaparan, Mokanarangan  and
      Nguyen, Thien Huu  and
      Penn, Gerald  and
      Ramesh, Arti  and
      Jana, Abhik",
    booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.textgraphs-1.10/",
    pages = "89--104",
    abstract = "Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. Although the news domain is subject to rapid changes over time, the temporal dynamics of the spreaders' language and network have not been explored yet. In this paper, we analyze the users' time-evolving semantic similarities and social interactions and show that such patterns can, on their own, indicate misinformation spreading. Building on these observations, we propose a dynamic graph-based framework that leverages the dynamic nature of the users' network for detecting fake news spreaders. We validate our design choice through qualitative analysis and demonstrate the contributions of our model{'}s components through a series of exploratory and ablative experiments on two datasets."
}