Qiang Sheng 盛强

I am a 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:

  • multi-modal fake news/misinformation detection
  • controversy detection
  • fact checking

Contact: shengqiang18z AT ict dot ac dot cn

CV    /    GitHub    /    Google Scholar    /    ResearchGate    /    ORCID    /    DBLP    /    Zhihu Column

News
09/2021 Served 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.
06/2021 Served as a reviewer for EMNLP 2021.
05/2021 One first-authored paper got accepted by ACL-IJCNLP 2021.
01/2021 One co-authored paper got accepted by TheWebConf 2021.
11/2020 One co-authored Chinese paper got accpeted by Journal of Computer Research and Development.
04/2020 One co-authored paper got accepted by ACL 2020.
Publications
2021
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 (To be Available)
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%)
Paper (To be Available)
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%)
PDF / Poster / Code / Chinese Dataset / Chinese Blog 1 / Chinese Blog 2
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
PDF
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 / Code / Chinese Video
TL;DR: We leverage both publisher emotion and social emotion for fake news detection.
2020
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%)
PDF / 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
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.
Teaching Experience
2020/2021 Spring Teaching Assistant, 081203M05008H: Multimedia Technology, UCAS
Academic Services
Reviewer/PC Member AAAI 2022
EMNLP 2021
ACM CSCW 2021
Information Processing and Management
Multimedia Systems
Student Volunteer IEEE ICDM 2019
Education
2018- Ph.D in Computer Science and Technology (Candidate)
School of Computer Science and Technology, University of Chinese Academy of Sciences
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
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