Kai Shu  

Gladwin Development Chair Assistant Professor

Department of Computer Science
College of Computing

Illinois Institute of Technology

Office: Stuart Building 226D

Mail: 10 W. 31st Street Room 226D, Chicago, IL 60616

E-mail: kshu at iit.edu
Twitter: @KaiShu0327

Google Scholar

 

News and Highlights

[5/2022] Received the DARPA AI Forward Scholarship.

[5/2022] One paper is accepted in KDD 2023.

[5/2023] Invited to serve as the PC Vice-Chair for Bench'23.

[4/2023] Received an IARPA grant on interpretable authorship attribution and obfuscation [IIT News].

[4/2023] Two papers are accepted in ACM SIGSIM-PADS’23.

[3/2023] Keynote invitation in BeyondFacts 2023 workshop, collocated in TheWebConf'23

[2/2023] AI 2000 Most Influential Scholar Award Honorable Mention in Data Mining, Aminer 2023.

[2/2023] Grateful to receive a NSF SaTC grant to study the computational approaches for disinformation.

[2/2023] One paper is accepted in TheWebConf 2023.

[1/2023] One paper is accepted in EACL 2023.

[11/2022] Selected for the AAAI-2023 New Faculty Highlights Program.

[10/2022] Received a DARPA grant on modeling misinformation pathways, joint with Charles River Analytics.

[9/2022] Invited to talk (remotely) at Tsinghua University and Georgetown University.

[9/2022] Invited to talk at Cisco Research Responsible AI Summit.

[8/2022] Received two IIT ACT Center Computational Interdisciplinary Seed Funding to study disinformation and pandemic modeling [IIT News].

[7/2022] Invited to serve as a Senior PC for AAAI 2023.

[6/2022] Invited to talk at Cisco Research on fair machine learning.

[5/2022] One paper is accepted in KDD 2022.

[4/2022] Our 2017 fake news survey paper is the most popular article in SIGKDD Explorations.

[4/2022] One paper is accepted in NAACL 2022.

[4/2022] One paper is accepted in IEEE TKDE (Minor Revision).

[4/2022] Received a Cisco Research Award to support our research on explainable AI with fairness and privacy.

Kai Shu is a Gladwin Development Chair Assistant Professor in the Department of Computer Science at Illinois Institute of Technology since Fall 2020. His research lies in machine learning, data mining, social computing with applications such as disinformation, education, and healthcare. He obtained his PhD in Computer Science at Arizona State University in July 2020, under the supervision of Professor Huan Liu. He was the recipients of 2023 DARPA AI Forward Scholarship, 2023 AAAI New Faculty Highlights, 2022 Cisco Faculty Research Award, the finalist of a 2022 Meta Research Award, 2020 ASU Engineering Dean's Dissertation Award, and 2020/2015 ASU CIDSE Doctoral Fellowship. He interned at Microsoft Research AI, Yahoo Research and HP Labs. His research is generously supported by federal agencies such as NSF (SaTC and CCF), DARPA (AIE), and IARPA (HIATUS), and industry sponsors such as Cisco, Google, DPI.

I am *ACTIVELY* looking for Postdocs and multiple self-motivated PhD students to conduct research in the area of data mining, machine learning, social media mining, and natural language processing. Interested students please feel free to drop me an email with your CV and transcript (and I can not reply to every individual email).

Research Interests 

  • AI for social good: dis-/mis-information, privacy, security, healthcare.
  • Trustworthy AI modeling: interpretability, robustness, fairness.
  • Learning with imperfect data: self-/weakly-supervised learning, meta learning, data augmentation.
  • Representation learning: text/graph mining, multi-modal learning, adversarial learning

Detecting Fake News on Social Media
    Table of Contents
    Order Hard-cover or PDF
    Tools and Datasets
     		     Disinformation, Misinformation and Fake News in Social Media     Table of Contents
    An Introductory Chapter
    [A Chinese Introduction Blog][Vito]

Selected Publications [Full List]

  • MUSER : A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection. [PDF]
    Hao Liao, JiaHao Peng, Zhanyi Huang, Wei Zhang, Guanghua Li, Kai Shu, and Xing Xie.
    Proceedings of 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), ADS Track.
  • Attacking Fake News Detectors via Manipulating News Social Engagement. [PDF]
    Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu and Kai Shu.
    Proceedings of The 2023 ACM Web Conference (WWW 2023).
  • PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners. [PDF]
    Canyu Chen, Kai Shu.
    Proceedings of The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023).
  • A Model-Agnostic Approach to Differentially Private Topic Mining. [PDF]
    Han Wang*, Jayashree Sharma*, Shuya Feng, Kai Shu, and Yuan Hong.
    Proceedings of 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)
  • WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding. [PDF][Code]
    Guoqing Zheng, Giannis Karamanolakis, Kai Shu, Ahmed Hassan Awadallah.
    Proceedings of 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2022)
  • "This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders. [PDF]
    Xinyi Zhou, Kai Shu, Vir V. Phoha, Huan Liu and Reza Zafarani.
    Proceedings of The 2022 ACM Web Conference (WWW 2022)
  • Domain Adaptive Fake News Detection via Reinforcement Learning. [PDF]
    Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu, Michelle Mancenido and Huan Liu.
    Proceedings of The 2022 ACM Web Conference (WWW 2022)
  • Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features. [PDF]
    Tianxiang Zhao, Enyan Dai, Kai Shu, and Suhang Wang
    Proceedings of 15th ACM International Conference on Web Search and Data Mining (WSDM 2022).
  • Causal Understanding of Fake News Dissemination on Social Media. [PDF]
    Lu Cheng, Ruocheng Guo, Kai Shu, and Huan Liu.
    Proceedings of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)
  • User Preference-aware Fake News Detection. [PDF][Code][PyG Example][DGL Example][Data][A video demo by DeepFindr]
    Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, and Lichao Sun.
    Proceedings of 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021) (Short paper)
  • Mining Dual Emotion for Fake News Detection. [PDF][Code]
    Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu.
    Proceedings of 30th The Web Conference (WWW 2021)
  • Fact-enhanced Synthetic News Generation. [PDF][Poster]
    Kai Shu*, Yichuan Li*, Kaize Ding, and Huan Liu.
    Proceedings of The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021)
  • Early Detection of Fake News with Multi-source Weak Social Supervision. [PDF]
    Kai Shu, Guoqing Zheng, Yichuan Li, Subhabrata Mukherjee, Ahmed Hassan Awadallah, Scott Ruston, and Huan Liu.
    Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020)
  • Learning with Weak Supervision for Email Intent Detection. [PDF]
    Kai Shu, Subhabrata Mukherjee*, Guoqing Zheng*, Ahmed Hassan Awadallah, Milad Shokouhi and Susan Dumais.
    Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020)
  • dEFEND: Explainable Fake News Detection. [PDF][Code]
    Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu.
    Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019)
    Media coverage: [Techxplore Today]
  • Fake News Detection on Social Media: A Data Mining Perspective. [PDF][Data]
    Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu.
    SIGKDD Explorations, 2017.