{"type":"ScholarlyArticle","title":"Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection","authors":[{"name":"Ziyi Kou","slug":"ziyi-kou","given":"Ziyi","family":"Kou","affiliation":"Unversity of Illinois at Urbana-Champaign"},{"name":"Lanyu Shang","slug":"lanyu-shang","given":"Lanyu","family":"Shang","affiliation":"Unversity of Illinois at Urbana-Champaign"},{"name":"Yang Zhang","slug":"yang-zhang","given":"Yang","family":"Zhang","affiliation":"University of Notre Dame {ziyikou2 ,"},{"name":"Zhenrui Yue","slug":"zhenrui-yue","given":"Zhenrui","family":"Yue","affiliation":"Unversity of Illinois at Urbana-Champaign"},{"name":"Huimin Zeng","slug":"huimin-zeng","given":"Huimin","family":"Zeng","affiliation":"Unversity of Illinois at Urbana-Champaign"},{"name":"Dong Wang","slug":"dong-wang","given":"Dong","family":"Wang","affiliation":"Unversity of Illinois at Urbana-Champaign"}],"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.","doi":"10.24963/ijcai.2022/706","url":"https://ijcai.org/proceedings/2022/706","pdf":"https://ijcai.org/proceedings/2022/0706.pdf","markdown":"https://ijcai.org/proceedings/2022/706.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/706","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":706,"pages":{"first":5087,"last":5093},"keywords":["Humans and AI: Applications","Humans and AI: Human-AI Collaboration"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987128","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985271","length_sec":716,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Al-Rakhami and Al-Amri, 2020] Mabrook S Al-Rakhami and Atif M Al-Amri. Lies kill, facts save: detecting covid- 19 misinformation in twitter. Ieee Access, 8:155961- 155970, 2020.","title":"Lies kill, facts save: detecting covid-19 misinformation in twitter","authors":"Atif M Al-Amri Mabrook S Al-Rakhami","year":"2020","venue":"Ieee Access","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Alenezi and Alqenaei, 2021] Mohammed N Alenezi and Zainab M Alqenaei. Machine learning in detecting covid- 19 misinformation on twitter. Future Internet, 13(10):244, 2021.","title":"Machine learning in detecting covid-19 misinformation on twitter","authors":"N Mohammed, Alenezi, M Zainab, Alqenaei","year":"2021","venue":"Future Internet","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ayoub et al., 2021] Jackie Ayoub, X Jessie Yang, and Feng Zhou. Combat covid-19 infodemic using explainable nat- ural language processing models. Information Processing & Management, 58(4):102569, 2021.","title":"Combat covid-19 infodemic using explainable natural language processing models","authors":"Jackie Ayoub, Jessie Yang, Feng Zhou","year":"2021","venue":"Information Processing & Management","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Barbu et al., 2019] Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz. Objectnet: A large- scale bias-controlled dataset for pushing the limits of ob- ject recognition models. Advances in neural information processing systems, 32, 2019.","title":"Objectnet: A largescale bias-controlled dataset for pushing the limits of object recognition models","authors":"Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brand et al., 2021] Erik Brand, Kevin Roitero, Michael So- prano, and Gianluca Demartini. E-bart: Jointly predicting and explaining truthfulness. TTO 2021, page 18, 2021.","title":"E-bart: Jointly predicting and explaining truthfulness","authors":"Brand","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Burton, 2004] Richard F Burton. Multiple choice and true/false tests: reliability measures and some implications of negative marking. Assessment & Evaluation in Higher Education, 29(5):585-595, 2004.","title":"Multiple choice and true/false tests: reliability measures and some implications of negative marking","authors":"Richard F Burton","year":"2004","venue":"Assessment & Evaluation in Higher Education","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2020] Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, and Chenliang Xu. Talking- head generation with rhythmic head motion. In European Conference on Computer Vision, pages 35-51. Springer, 2020.","title":"Talkinghead generation with rhythmic head motion","authors":"Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cheng et al., 2021] Mingxi Cheng, Songli Wang, Xiaofeng Yan, Tianqi Yang, Wenshuo Wang, Zehao Huang, Xiongye Xiao, Shahin Nazarian, and Paul Bogdan. A covid-19 ru- mor dataset. Frontiers in Psychology, 12, 2021.","title":"A covid-19 rumor dataset","authors":"Cheng","year":"2021","venue":"Frontiers in Psychology","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cui et al., 2020] Limeng Cui, Haeseung Seo, Maryam Tabar, Fenglong Ma, Suhang Wang, and Dongwon Lee. Deterrent: Knowledge guided graph attention network for detecting healthcare misinformation. In Proceedings of the 26th ACM SIGKDD international conference on knowl- edge discovery & data mining, pages 492-502, 2020.","title":"Deterrent: Knowledge guided graph attention network for detecting healthcare misinformation","authors":"Cui","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Danilevsky et al., 2020] Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. A survey of the state of explainable ai for natural lan- guage processing. arXiv preprint arXiv:2010.00711, 2020.","title":"A survey of the state of explainable ai for natural language processing","authors":"Marina Danilevsky, Ranit Kun Qian, Yannis Aharonov, Ban Katsis, Prithviraj Kawas, Sen","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Islam et al., 2020] Md Saiful Islam, Tonmoy Sarkar, Saz- zad Hossain Khan, Abu-Hena Mostofa Kamal, SM Mur- shid Hasan, Alamgir Kabir, Dalia Yeasmin, Moham- mad Ariful Islam, Kamal Ibne Amin Chowdhury, Kazi Se- lim Anwar, et al. Covid-19-related infodemic and its im- pact on public health: A global social media analysis. The American journal of tropical medicine and hygiene, 103(4):1621, 2020.","title":"Covid-19-related infodemic and its impact on public health: A global social media analysis","authors":"Md Saiful Islam, Tonmoy Sarkar, Abu-Hena Mostofa Sazzad Hossain Khan, Kamal, Alamgir Sm Murshid Hasan, Dalia Kabir, Mohammad Ariful Yeasmin, Kamal Islam, Ibne Amin, Kazi Chowdhury, Selim Anwar","year":"2020","venue":"The American journal of tropical medicine and hygiene","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jain and Wallace, 2019] Sarthak Jain and Byron C Wal- lace. Attention is not explanation. arXiv preprint arXiv:1902.10186, 2019.","title":"Attention is not explanation","authors":"Sarthak Jain, Byron C Wallace","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Karagiannis et al., 2020] Georgios Karagiannis, Mo- hammed Saeed, Paolo Papotti, and Immanuel Trummer. Scrutinizer: A mixed-initiative approach to large- scale, data-driven claim verification. arXiv preprint arXiv:2003.06708, 2020.","title":"Scrutinizer: A mixed-initiative approach to largescale, data-driven claim verification","authors":"Georgios Karagiannis, Mohammed Saeed, Paolo Papotti, Immanuel Trummer","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kou et al., 2022] Ziyi Kou, Lanyu Shang, Yang Zhang, and Dong Wang. Hc-covid: A hierarchical crowdsource knowledge graph approach to explainable covid-19 misin- formation detection. Proceedings of the ACM on Human- Computer Interaction, 6(GROUP):1-25, 2022.","title":"Hc-covid: A hierarchical crowdsource knowledge graph approach to explainable covid-19 misinformation detection","authors":"Ziyi Kou, Lanyu Shang, Yang Zhang, Dong Wang","year":"2022","venue":"Proceedings of the ACM on Human-Computer Interaction","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ma et al., 2019] Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, and Dawei Song. A tensorized transformer for language modeling. Advances in Neural Information Processing Systems, 32, 2019.","title":"A tensorized transformer for language modeling","authors":"Ma","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Méndez Méndez et al., 2019] Ana Elisa Méndez Méndez, Mark Cartwright, and Juan Pablo Bello. Machine-crowd- expert model for increasing user engagement and annota- tion quality. In Extended Abstracts of the 2019 CHI Con- ference on Human Factors in Computing Systems, pages 1-6, 2019.","title":"Machine-crowdexpert model for increasing user engagement and annotation quality","authors":"Ana Elisa, Méndez Méndez, Mark Cartwright, Juan Pablo Bello","year":"2019","venue":"Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Patwa et al., 2021] Parth Patwa, Mohit Bhardwaj, Vineeth Guptha, Gitanjali Kumari, Shivam Sharma, Srinivas PYKL, Amitava Das, Asif Ekbal, Shad Akhtar, and Tan- moy Chakraborty. Overview of constraint 2021 shared tasks: Detecting english covid-19 fake news and hindi hos- tile posts. In Proceedings of the First Workshop on Com- bating Online Hostile Posts in Regional Languages during Emergency Situation (CONSTRAINT). Springer, 2021.","title":"Overview of constraint 2021 shared tasks: Detecting english covid-19 fake news and hindi hostile posts","authors":"Parth Patwa, Mohit Bhardwaj, Vineeth Guptha, Gitanjali Kumari, Shivam Sharma, Pykl Srinivas, Amitava Das, Asif Ekbal, Shad Akhtar, Tanmoy Chakraborty","year":"2021","venue":"Proceedings of the First Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (CONSTRAINT)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Shang et al., 2022a] Lanyu Shang, Ziyi Kou, Yang Zhang, Jin Chen, and Dong Wang. A privacy-aware distributed knowledge graph approach to qois-driven covid-19 mis- information detection. In 2022 IEEE/ACM 30th Interna- tional Symposium on Quality of Service (IWQoS). IEEE, 2022.","title":"A privacy-aware distributed knowledge graph approach to qois-driven covid-19 misinformation detection","authors":"Shang","year":"2022","venue":"IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Shang et al., 2022b] Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. A duo-generative approach to explain- able multimodal covid-19 misinformation detection. In Proceedings of the ACM Web Conference 2022, pages 3623-3631, 2022.","title":"A duo-generative approach to explainable multimodal covid-19 misinformation detection","authors":"Shang","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2019] Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 950-958, 2019.","title":"Kgat: Knowledge graph attention network for recommendation","authors":"Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhou et al., 2020] Yuanen Zhou, Meng Wang, Daqing Liu, Zhenzhen Hu, and Hanwang Zhang. More grounded im- age captioning by distilling image-text matching model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4777-4786, 2020.","title":"More grounded image captioning by distilling image-text matching model","authors":"Yuanen Zhou, Meng Wang, Daqing Liu, Zhenzhen Hu, Hanwang Zhang","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}