TY - GEN
T1 - DISCO
T2 - 31st ACM International Conference on Information and Knowledge Management, CIKM 2022
AU - Fu, Dongqi
AU - Ban, Yikun
AU - Tong, Hanghang
AU - MacIejewski, Ross
AU - He, Jingrui
N1 - Funding Information:
In this paper, we identify disinformation detection challenges and make an attempt for proposing DISCO and demonstrate it. We wish that the next generation of disinformation detection systems could be able to simultaneously detect and explain during the whole life cycle of disinformation dissemination. ACKNOWLEDGEMENT This work is supported by the National Science Foundation (Award Number IIS-1947203, IIS-2117902, and IIS-2137468), and the U.S. Department of Homeland Security (Award Number 17STQAC00001-05-00). The views and conclusions are those of the authors and should not be interpreted as representing the official policies of the funding agencies or the government.
Publisher Copyright:
© 2022 ACM.
PY - 2022/10/17
Y1 - 2022/10/17
N2 - 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.
AB - 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.
KW - disinformation detection
KW - explanation
KW - graph augmentation
UR - http://www.scopus.com/inward/record.url?scp=85140824543&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140824543&partnerID=8YFLogxK
U2 - 10.1145/3511808.3557202
DO - 10.1145/3511808.3557202
M3 - Conference contribution
AN - SCOPUS:85140824543
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 4848
EP - 4852
BT - CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 17 October 2022 through 21 October 2022
ER -