Qiang Sheng 盛强

I am a fourth-year PhD student in Computer Science at the University of Chinese Academy of Sciences (UCAS). I am doing research at the Multimedia Computing Group, Institute of Computing Technology, CAS. My advisor is Professor Juan Cao. My research interests include:

  • fake news/misinformation detection
  • fact checking
  • controversy detection

Contact: shengqiang18z AT ict dot ac dot cn

Currículum Vitae    /    GitHub    /    Google Scholar    /    ResearchGate    /    ORCID    /    DBLP

Zhihu Column    /    ACL Anthology    /    IR Anthology

04/2022 Will give a spotlight presentation on our ACL 2022 work at the AIS 2022.
04/2022 One first-authored paper got accepted by Information Processing and Management.
04/2022 Will give talks on our ACL 2022 work at the BUAFAI 2022 and AI TIME Live.
04/2022 One co-authored paper got accepted by SIGIR 2022.
02/2022 One first-authored paper got accepted by ACL 2022.
12/2021 One co-authored paper got accepted by AAAI 2022.
10/2021 Invited to serve as a Reviewer for ACL Rolling Review.
09/2021 Invited to serve as a PC Member for AAAI 2022.
08/2021 One co-authored paper got accepted by ACM MM 2021 Industrial Track.
08/2021 One co-first-authored paper got accepted by ACM CIKM 2021.
Full List
Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
Qiang Sheng, Juan Cao, H. Russell Bernard, Kai Shu, Jintao Li, and Huan Liu
Information Processing and Management (IP&M), 2022
Preprint / Paper / Code / Dataset / Chinese Brief
TL;DR: Based on the largest false news dataset on Chinese Weibo (44k+), we characterize the spread of false news in nine domains and analyze the underlying user effects.
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, and Fuzhen Zhuang
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022) (Acceptance Rate:165/667=24.7%)
Preprint / Paper (TBA)
TL;DR: We reveal the entity bias in fake news detection datasets and propose a causal framework to mitigate such bias for better generalization to future data.
Zoom Out and Observe: News Environment Perception for Fake News Detection
Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang, and Yongchun Zhu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)
Preprint / Paper / Poster / Code / Chinese Video / Chinese Blog
TL;DR: For the first time, we propose to perceive signals from the news environment for fake news detection.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection
Guang Yang, Juan Cao, Qiang Sheng, Peng Qi, Xirong Li, and Jintao Li
Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022) (Acceptance Rate:1349/9020=15.0%)
Preprint / Paper / English Video
TL;DR: We propose a GCN-based method to dynamically find out crucial privacy-indicative regions for privacy-leaking image detection.
Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues
Peng Qi, Juan Cao, Xirong Li, Huan Liu, Qiang Sheng, Xiaoyue Mi, Qin He, Yongbiao Lv, Chenyang Guo, and Yingchao Yu
Proceedings of the 29th ACM International Conference on Multimedia (MM 2021)
Preprint / Paper / Chinese Blog 1/ 2
TL;DR: We point out three valuable text-image correlations in multimodal fake news and propose an entity-enhanced fusion framework for cross-modal correlation modeling in fake news detection.
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning
Qiang Sheng*, Xueyao Zhang*, Juan Cao, and Lei Zhong (*:Equal Contribution)
Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) (Acceptance Rate:271/1251=21.7%)
Preprint / Paper / Poster / Code / Datasets / Chinese Blog 1/ 2/ 3
TL;DR: We propose a graph-based model preference learning framework to separately handle the pattern and fact indicators in fake news detection.
Article Reranking by Memory-enhanced Key Sentence Matching for Detecting Previously Fact-checked Claims
Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, and Lei Zhong
Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) (Acceptance Rate:571/2327=24.5%)
Preprint / Paper / Poster / Code / Chinese Dataset / Chinese Blog 1/ 2/ 3
TL;DR: We detect previously fact-checked claims by matching them against the key sentences in fact-checking articles.
Semantics-Enhanced Multi-modal Fake News Detection (语义增强的多模态虚假新闻检测)
Peng Qi, Juan Cao, and Qiang Sheng
Journal of Computer Reasearch and Development, 2021
TL;DR: We focus on the role of explicit semantics for fake news detection via visual semantic extraction and knowledge-guided multi-modal semantic interaction.
Mining Dual Emotion for Fake News Detection
Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu
Proceedings of the 30th Web Conference (WWW 2021) (Acceptance Rate:357/1736=20.6%)
Preprint / Paper / Code / Chinese Video
TL;DR: We leverage both publisher emotion and social emotion for fake news detection.
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, and Ziang Wang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020) (Acceptance Rate:571/2244=25.4%)
Paper / Chinese Dataset
TL;DR: We propose a GCN-based method for post-level controversy detection. Also, we release the first Chinese dataset for this task.
Fake News and Disinformation Detection on the Internet: Recent Advances and Future Prospects (互联网虚假信息检测进展与展望)
Juan Cao, Qiang Sheng, and Peng Qi
Communications of the China Computer Federation, March 2020
Webpage Version / Chinese Blog
TL;DR: We review the recent advances of detecting fake news and disinformation and propose potential directions.
Exploring the Role of Visual Content in Fake News Detection
Juan Cao, Peng Qi, Qiang Sheng, Tianyun Yang, Junbo Guo, and Jintao Li
Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities, Lecture Notes in Social Network (LNSN)
Preprint / Springer Link
TL;DR: A survey chapter that focuses on the visual information for fake news detection.
2022/5/14 AIS 2022 (NLU Session) (Event / Session Video)
2022/4/24 BUAFAI 2022 (NLP & Speech Forum) (WeChat Article / Chinese Video)
2022/4/20 AI TIME Live (WeChat Article / Chinese Video / Chinese Blog)
Teaching Experience
2020/2021 Spring Teaching Assistant, 081203M05008H: Multimedia Technology, UCAS
Academic Services
Reviewer/PC Member NAACL 2022
ACL 2022
ACL Rolling Review (Nov. 2021~)
AAAI 2022
EMNLP 2021
ACM CSCW 2021 (Recognized for Excellent Reviewing)
Information Processing and Management
Multimedia Systems
Student Volunteer IEEE ICDM 2019
2018- Ph.D in Computer Science and Technology (Candidate)
University of Chinese Academy of Sciences (Cultivation Unit: Institute of Computing Technology, CAS)
2014-2018 B. Eng in Communication Engineering
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications
2011-2014 Weifang No.1 Middle School
Honors and Awards
2022 Pacemaker to Merit Student of UCAS, UCAS
2022 E Fund Scholarship, Guangdong E Fund Education Foundation & ICT, CAS
2019, 2020 Merit Student, University of Chinese Academy of Sciences
2018 Outstanding Graduate, Beijing Municipal Commission of Education
2015, 2016, 2017 National Undergraduate Scholarship, Ministry of Education of China