Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection
Abstract
In this paper, we study an explainable COVID-19 misinformation detection problem where the goal is to accurately identify COVID-19 misleading posts on social media and explain the posts with natural language explanations (NLEs). Our problem is motivated by the limitations of current explainable misinformation detection approaches that cannot provide NLEs for COVID-19 posts due to the lack of sufficient professional COVID-19 knowledge for supervision. To address such a limitation, we develop CEA-COVID, a crowd-expert-AI framework that jointly exploits the common logical reasoning ability of online crowd workers and the professional knowledge of COVID-19 experts to effectively generate NLEs for detecting and explaining COVID-19 misinformation. We evaluate CEA-COVID using two public COVID-19 misinformation datasets on social media. Results demonstrate that CEA-COVID outperforms existing explainable misinformation detection models in terms of both explainability and detection accuracy.
Cite
Text
Kou et al. "Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/706Markdown
[Kou et al. "Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/kou2022ijcai-crowd/) doi:10.24963/IJCAI.2022/706BibTeX
@inproceedings{kou2022ijcai-crowd,
title = {{Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection}},
author = {Kou, Ziyi and Shang, Lanyu and Zhang, Yang and Yue, Zhenrui and Zeng, Huimin and Wang, Dong},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5087-5093},
doi = {10.24963/IJCAI.2022/706},
url = {https://mlanthology.org/ijcai/2022/kou2022ijcai-crowd/}
}