@inproceedings{1fcbd0515b234d2dad8aa0f581279e70,
title = "DISCO: Comprehensive and Explainable Disinformation Detection",
abstract = "Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets. In this paper, we identify prevalent challenges and advances related to automated disinformation detection from multiple aspects and propose a comprehensive and explainable disinformation detection framework called DISCO. It leverages the heterogeneity of disinformation and addresses the opaqueness of prediction. Then we provide a demonstration of DISCO on a real-world fake news detection task with satisfactory detection accuracy and explanation. The demo video and source code of DISCO is now publicly available https://github.com/DongqiFu/DISCO. We expect that our demo could pave the way for addressing the limitations of identification, comprehension, and explainability as a whole.",
keywords = "disinformation detection, explanation, graph augmentation",
author = "Dongqi Fu and Yikun Ban and Hanghang Tong and Ross MacIejewski and Jingrui He",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 31st ACM International Conference on Information and Knowledge Management, CIKM 2022 ; Conference date: 17-10-2022 Through 21-10-2022",
year = "2022",
month = oct,
day = "17",
doi = "10.1145/3511808.3557202",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "4848--4852",
booktitle = "CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management",
address = "United States",
}