Kai Shu  

Gladwin Development Chair Assistant Professor

Department of Computer Science
College of Computing

Illinois Institute of Technology


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

Google Scholar
Curriculum Vitae

 

News and Highlights

[7/2020] Invited to serve as a PC member for TheWebConf 2021.

[7/2020] Two papers are accepted in CIKM 2020.

[7/2020] An algorithm to detect fake news-ASU Knowledge Enterprise.

[7/2020] Invited to serve as a PC member for SDM 2021.

[6/2020] Invited to speak at the 1st WHO Infodemiology Conference.[Where is Kai?]

[6/2020] Received the Best Reviewer Award in ICWSM 2020.

[6/2020] Invited to serve as a PC member for WSDM 2021.

[6/2020] Two papers are accepted in ECML-PKDD 2020.

[5/2020] FakeNewsTracker is among the Top ML Projects To Fight Fake News Fatigue During COVID-19.

[5/2020] One paper is accepted in IEEE Intelligent Systems. Media coverage: Venturebeat, Digital Information World, Report Door.

[5/2020] Invited to deliver research talks (remotely) in Google Research, Illinois Tech, and ICT, CAS.

[5/2020] Co-presented (remotely) a keynote talk in the Workshop on Data Science for Fake News at PAKDD 2020.

[4/2020] One paper is accepted in SIGIR'20.

[4/2020] Microsoft claims its AI framework spots fake news better than state-of-the-art baselines.

[3/2020] Received the ASU CIDSE Doctoral Fellowship.

[3/2020] The Springer book is officially out! - Disinformation, Misinformation, and Fake News in Social Media. [ToC]

[11/2019] One paper is accepted in ICWSM'20

[9/2019] Fires in the Amazon: Arizona researchers determine what’s true, what’s not, an interview with Arizona PBS Cronkite News on AI and fake news [video].

[9/2019] AI Squares Off Against Fake News. -- feature news about KDD'19 tutorial.

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 2020/2015 ASU CIDSE Doctoral Fellowship, 2018 SBP Disinformation Challenge Winner. He interned at Microsoft Research AI, Yahoo Research and HP Labs.

I am actively looking for self-motivated PhD students to conduct research in the area of data mining, machine learning and social media mining. Interested students please feel free to drop me an email with your CV and transcript.

CALL FOR PAPERS:

Research Interests 

  • AI for social good: disinformation/misinformation, user privacy, security.
  • Responsible AI systems: interpretable, robust, fair.
  • Learning with limited and noisy data: weak supervision, data generation, meta learning.
  • Representation Learning: NLP/graph, multi-modality learning, domain adaptation.

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]

  • Early Detection of Fake News with Multi-sourceWeak 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)
  • Detecting Fake News with Weak Social Supervision. [PDF]
    Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, and Huan Liu.
    IEEE Intelligent Systems, 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)
  • Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation. [PDF]
    Kai Shu, Deepak Mahudeswaran, Suhang Wang, and Huan Liu.
    Proceedings of 14th the International AAAI Conference on Web and Social Media (ICWSM 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]
  • Unsupervised Fake News Detection on Social Media: A Generative Approach. [PDF][Code]
    Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, and Huan Liu.
    Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI 2019) .
  • Beyond News Content: The Role of Social Context for Fake News Detection.
    [PDF][Poster]
    Kai Shu, Suhang Wang, and Huan Liu.
    Proceedings of 12th ACM International Conference on Web Search and Data Mining (WSDM 2019).
    Media coverage: [KDnuggets] [The Morning Paper]
  • Deep Headline Generation for Clickbait Detection. [PDF][Slides]
    Kai Shu, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu.
    The 2018 IEEE International Conference on Data Mining (ICDM 2018) (Regular Paper)
  • Multimodal Fusion of Brain Networks with Longitudinal Couplings. [PDF]
    Wen Zhang, Kai Shu, Suhang Wang, Huan Liu, and Yalin Wang.
    21th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018).
  • CrossFire: Cross Media Joint Friend and Item Recommendations. [PDF][Slides][Poster]
    Kai Shu, Suhang Wang, Jiliang Tang, Yilin Wang, and Huan Liu.
    Proceedings of 11th ACM International Conference on Web Search and Data Mining (WSDM 2018).
  • 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.