Preprints

1. Can Knowledge Editing Really Correct Hallucinations?, arXiv:2410.16251
   
2. Understanding The Concerns and Choices of The Public When Using Large Language Models for Healthcare, arXiv:2401.09090
   
3. Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective, arXiv:2309.12363
   
4. Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation, arXiv:2309.08793
   
5. Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues, arXiv:2211.05289
   
6. On Fair Classification with Mostly Private Sensitive Attributes, arXiv:2207.08336
   

Tutorials

Books  Journals  Conferences  Workshops

1. Fake News Research: Theories, Detection Strategies, and Open Problems.
   [PDF][Website]
   Reza Zafarani, Xinyi Zhou, Kai Shu, and Huan Liu.
   25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019).
   Media coverage: [A Blog in Japanese][Datanami]


2. Fake News: Fundamental Theories, Detection Strategies and Challenges.
   [PDF][Website]
   Xinyi Zhou, Reza Zafarani, Kai Shu, and Huan Liu.
   Proceedings of 12th ACM International Conference on Web Search and Data Mining (WSDM 2019).
   Media coverage: [Psychology Today]

Books or Book Chapters

Tutorials  Journals  Conferences  Workshops

1. Cross-Domain Fake News Detection on Social Media: A Context-Aware Adversarial Approach.
   Kai Shu, Ahmadreza Mosallanezhad, and Huan Liu.
   Chapter, in Frontiers in Fake Media Generation and Detection, Springer Press, 2022. Forthcoming.


2. Combating Online Hostile Posts in Regional Languages during Emergency Situation.
   Tanmoy Chakraborty, Kai Shu, H. Russell Bernard, Huan Liu, and Md Shad Akhtar (Eds.).
   First International Workshop, CONSTRAINT 2021 Collocated with AAAI 2021Virtual Event, February 8, 2021, Revised Selected Papers. Communications in Computer and Information Science 1402, ISBN 978-3-030-73695-8.


3. Disinformation, Misinformation, and Fake News in Social Media - Emerging Research Challenges and Opportunities.
   Kai Shu, Suhang Wang, Dongwon Lee, and Huan Liu (Editors).
   Lecture Notes in Social Network (LNSN), Springer Press, 2020. DOI https://doi.org/10.1007/978-3-030-42699-6.


4. Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements. [PDF][A Chinese Blog from Zhuanzhi]
   Kai Shu, Suhang Wang, Dongwon Lee, and Huan Liu.
   Chapter, in Lecture Notes in Social Network (LNSN), Springer Press, 2020.


5. Detecting Fake News on Social Media. [Flyer][Website][Sun Devil Shelf]
   Kai Shu and Huan Liu.
   Morgan & Claypool Publishers, 2019. DOI: 10.2200/S00926ED1V01Y201906DMK018


6. Studying Fake News via Network Analysis: Detection and Mitigation. [PDF][A Course Blog from Cornell University]
   Kai Shu, H. Russell Bernard, and Huan Liu.
   Lecture Notes in Social Network (LNSN), Springer Press, 2018.

Journal Papers and Magazines

Tutorials  Books  Conferences  Workshops

1. Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges. [PDF][Blog]
   Baixiang Huang, Canyu Chen and Kai Shu
   SIGKDD Explorations, 2024.


2. EML: Emotion-Aware Meta Learning for Cross-Event False Information Detection. [PDF]
   Yinqiu Huang, Min Gao, Kai Shu, Chenghua Lin, Jia Wang, and Wei Zhou.
   ACM Transactions on Knowledge Discovery from Data (TKDD), 2024.


3. Combating Misinformation in the Age of LLMs: Opportunities and Challenges. [PDF]
   Canyu Chen, Kai Shu.
   AI Magazine, AAAI, 2024.



2023

4. PyGOD: A Python Library for Graph Outlier Detection. [PDF]
   Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
   Journal of Machine Learning Research (JMLR 2023), 2023 (Minor Revision).
   


5. Meta-Prompt Based Learning for Low-Resource False Information Detection. [PDF]
   Yinqiu Huang, Min Gao, Jia Wang, Junwei Yin, Kai Shu, Qilin Fan, and Junhao Wen.
   Information Processing and Management (IPM), Elsevier, 2023.
   


6. Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach. [PDF]
   Yueqing Liang, Canyu Chen, Tian Tian, and Kai Shu
   Frontiers in Big Data, section Data Mining and Management, 2023
   


7. FinD: Fine-Grained Discrepancy-based Fake News Detection. [PDF]
   Jia Wang, Min Gao, Yinqiu Huang, Kai Shu, and Hualing Yi.
   Computer Speech and Language, 2022
   

2022

1. A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues. [PDF]
   A.R. Sana Ullah, Anupam Das, Anik Das, Muhammad Ashad Kabir, Kai Shu.
   Social Network Analysis and Mining, Springer, 2022
   


2. Artificial Intelligence Algorithms for Treatment of Diabetes. [PDF]
   Mudassir Rashid, Mohammad Reza Askari, Canyu Chen, Yueqing Liang, Kai Shu, and Ali Cinar
   Algorithms, 2022.
   


3. Characterizing Multi-domain False News and Underlying User Effects on Chinese Weibo. [PDF]
   Qiang Sheng, Juan Cao, H. Russell Bernard, Kai Shu, Jintao Li, and Huan Liu
   Information Processing and Management (IPM), Elsevier, 2022.
   


4. Memory-Guided Multi-View Multi-Domain Fake News Detection. [PDF]
   Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Jindong Wang, Minghui Wu, and Fuzhen Zhuang
   IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.
   


5. Combating Disinformation on Social Media: A Computational Perspective. [PDF]
   Kai Shu
   BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2022.
   

2021

1. A public health research agenda for managing infodemics: Methods and results of the first WHO infodemiology conference. [PDF]
   Neville Calleja, Neetu Abad, ..., Kai Shu, ..., Tina D Purnat.
   JIMR Infodemiology, 2021.
   


2. Cross-domain Graph Anomaly Detection. [PDF]
   Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, and Huan Liu
   IEEE Transactions on Neural Networks and Learning Systems, 2021.
   


3. Temporally Evolving Graph Neural Network for Fake News Detection. [PDF]
   Chenguang Song, Kai Shu, and Bin Wu
   Information Processing and Management (IPM), Elsevier, 2021.
   


4. Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges. [PDF][Chinese Version]
   Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu
   Journal of Computer Research and Development, 2021
   


5. Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning. [PDF]
   Wen Zhang, Brittany Blair Braden, Gustavo Miranda, Kai Shu, Suhang Wang, Huan Liu, and Yalin Wang
   Neuroinformatics, 2021.
   

2020

1. Combating Disinformation in a Social Media Age. [PDF][Top Cited Paper][Top Downloaded Paper]
   Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, and Huan Liu.
   WIREs Data Mining and Knowledge Discovery, 2020.
   


2. Detecting Fake News with Weak Social Supervision. [PDF]
   Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, and Huan Liu.
   IEEE Intelligent Systems, 2020.
   Media coverage: [Venturebeat] [Digital Information World] [Report Door]


3. FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media. [PDF]
   Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu.
   Journal of Big Data, Mary Ann Liebert, Inc. Publishers, 2020


4. Joint Spatial and Temporal Modeling for Hydrological Prediction. [PDF]
   Qun Zhao, Yuelong Zhu, Kai Shu, Dingsheng Wan, Yufeng Yu, Xudong Zhou, and Huan Liu.
   IEEE Access, 2020.

Before 2020

1. Exploring Correlation Network for Cheating Detection. [PDF]
   Ping Luo, Kai Shu, Junjie Wu, Li Wan, and Yong Tan.
   ACM TIST, 2019.


2. FakeNewsTracker: A Tool for Fake News Collection, Detection, and Visualization. [PDF]Poster][Demo]
   Kai Shu, Deepak Mahudeswaran, and Huan Liu.
   Computational and Mathematical Organization Theory, 2019 (CMOT 2019). Best of SBP.


3. Towards Privacy Preserving Social Recommendation under Personalized Privacy Settings. [PDF]
   Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, and Yujun Zhang.
   World Wide Web Journal, 2018.


4. 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.
   Included in the curriculum at: [Worcester Polytechnic Institute (WPI)][The Hang Seng University of Hong Kong]
   Media coverage: [KDnuggets][ASLIFacebook][Data Skeptic][A Chinese Blog][Another Chinese Blog][A Korean Blog][Wikipedia][Hackernoon]
   


5. User Identity Linkage across Online Social Networks: A Review. [PDF]
   Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani, and Huan Liu.
   SIGKDD Explorations, 2016.

Conference Papers

Tutorials  Books  Journals  Workshops

2024

1. Can Large Language Model Agents Simulate Human Trust Behavior?. [PDF]
   Chengxing Xie*, Canyu Chen*, Feiran Jia, Ziyu Ye, Kai Shu, Adel Bibi, Ziniu Hu, Philip Torr, Bernard Ghanem, Guohao Li
   Proceedings of The 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024).


2. Can Large Language Models Identify Authorship. [PDF]
   Baixiang Huang, Canyu Chen and Kai Shu
   Findings of The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024-Findings).


3. MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection. [PDF]
   Xiongxiao Xu, Kaize Ding, Canyu Chen and Kai Shu
   Proceedings of The 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024).


4. Model Attribution in Machine-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning. [PDF]
   Alimohammad Beigi, Zhen Tan, Nivedh Mudiam, Canyu Chen, Kai Shu and Huan Liu
   Proceedings of The 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024).


5. Backdoor Activation Attack: Attack Large Language Models using Activation Steering for Safety-Alignment. [PDF]
   Haoran Wang, Kai Shu
   Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024).


6. Investigating Gender Euphoria and Dysphoria on TikTok: Characterization and Comparison. [PDF]
   Sj Dillon*, Yueqing Liang*, H. Russell Bernard, Kai Shu
   Proceedings of 16th International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2024).


7. TrustLLM: Trustworthiness in Large Language Models. [PDF][Benchmark]
   Lichao Sun, and many others including Kai Shu.
   Proceedings of the 2024 International Conference on Machine Learning (ICML 2024). Position Paper.


8. Surrogate Modeling for HPC Application Iteration Times Forecasting with Network Features. [PDF]
   Xiongxiao Xu, Kevin A. Brown, Tanwi Mallick, Xin Wang, Elkin Cruz-Camacho, Robert B. Ross, Christopher D. Carothers, Zhiling Lan and Kai Shu.
   Proceedings of The 38th ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2024).
   


9. From Creation to Clarification: ChatGPT's Journey Through the Fake News Quagmire. [PDF]
   Yue Huang, Kai Shu, Philip S. Yu and Lichao Sun.
   Companion Proceedings of The 2024 ACM Web Conference (WWW 2024). Short paper.


10. Can LLM-Generated Misinformation Be Detected? [PDF]
    Canyu Chen, Kai Shu.
    Proceedings of the 12th International Conference on Learning Representations (ICLR 2024).


11. Fine-Grained Discrepancy Contrastive Learning for Robust Fake News Detection. [PDF]
    Junwei Yin, Min Gao, Kai Shu, Jia Wang, Yinqiu Huang, Wei Zhou.
    Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024).
    

2023

1. Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models. [PDF]
   Haoran Wang, and Kai Shu.
   Findings of The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023-Findings).
   


2. TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks. [PDF][Long version][Github]
   Baixiang Huang, Bryan Hooi and Kai Shu.
   Proceedings of 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2023 (SIGSPATIAL 2023).
   


3. 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.
   


4. Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation. [PDF]
   Xiongxiao Xu, Xin Wang, Elkin Cruz-Camacho, Christopher D. Carothers, Kevin A. Brown, Robert B. Ross, Zhiling Lan and Kai Shu.
   Proceedings of The 37th ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2023).
   


5. Hybrid PDES Simulation of HPC Networks using Zombie Packets. [PDF]
   Elkin Cruz-Camacho, Kevin A. Brown, Xin Wang, Xiongxiao Xu, Kai Shu, Zhiling Lan, Robert B. Ross and Christopher D. Carothers.
   Proceedings of The 37th ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2023). Best Short Paper.
   


6. Attacking Fake News Detectors via Manipulating News Social Engagement. [PDF][A Blog on Montreal AI]
   Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu and Kai Shu.
   Proceedings of The 2023 ACM Web Conference (WWW 2023).
   


7. PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners. [PDF]
   Canyu Chen, and Kai Shu.
   Proceedings of The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023).
   

2022

1. Nothing Stands Alone: Leveraging News Relations through a Hypergraph for Fake News Detection. [PDF]
   Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, and Huan Liu.
   Proceedings of the 2022 IEEE International Conference on Big Data (IEEE BigData 2022).
   


2. BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs. [PDF]
   Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
   Proceedings of The 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), Track on Datasets and Benchmarks.
   


3. 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).
   


4. 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).


5. "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)


6. 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)


7. 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).
   

2021

1. Generating Topic-Preserving Synthetic News. [PDF]
   Ahmadreza Mosallanezhad, Kai Shu, and Huan Liu.
   Proceedings of the 2021 IEEE International Conference on Big Data (IEEE BigData 2021).
   


2. Multi-Source Domain Adaptation with Weak Supervision for Early Fake News Detection. [PDF]
   Yichuan Li, Kyumin Lee, Nima Kordzadeh, Brenton Faber, Cameron Fiddes, Elaine Chen, and Kai Shu.
   Proceedings of the 2021 IEEE International Conference on Big Data (IEEE BigData 2021).
   


3. DAFD: Domain Adaptation Framework for Fake News Detection. [PDF]
   Yinqiu Huang, Min Gao, Jia Wang, and Kai Shu.
   Proceedings of the 28th International Conference on Neural Information Processing (ICONIP 2021).
   


4. Data Generation for Neural Disinformation Detection. [PDF]
   Tharindu Kumarage, Amrita Bhattacharjee, Kai Shu, and Huan Liu.
   International Conference on Social Computing, Behavior-Culture Modeling, and Prediction (SBP-BRiMS 2021), working paper track.
   


5. 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)
   


6. Labeled Data Generation with Inexact Supervision. [PDF]
   Enyan Dai, Kai Shu, Yiwei Sun, and Suhang Wang.
   Proceedings of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)
   


7. User Preference-aware Fake News Detection. [PDF]
   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)


8. Mining Dual Emotion for Fake News Detection. [PDF]
   Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu.
   Proceedings of 30th The Web Conference (WWW 2021)
   Media coverage: [Medium][AI Scholar]


9. 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)

2020

1. Authorship Attribution for Neural Text Generation. [PDF]
   Adaku Uchendu, Thai Le, Kai Shu and Dongwon Lee
   Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)


2. Unsupervised Cyberbullying Detection via Time-Informed Deep Clustering. [PDF]
   Lu Cheng, Kai Shu, Siqi Wu, Yasin Silva, Deborah Hall and Huan Liu
   Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020)


3. Graph Prototypical Networks for Few-shot Learning on Attributed Networks. [PDF]
   Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu and Huan Liu
   Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM 2020)


4. 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 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020) Media coverage: [Venturebeat]


5. Spatial Community-Informed Evolving Graphs for Demand Prediction. [PDF]
   Qianru Wang, Bin Guo, Yi Ouyang, Kai Shu, Zhiwen Yu and Huan Liu.
   Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020)


6. 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)


7. Joint Local and Global Sequence Modeling in Temporal Correlation Networks for Trending Topic Detection. [PDF]
   Kai Shu, Liangda Li, Suhang Wang, Yunhong Zhou, and Huan Liu.
   Proceedings of 12th ACM Web Science Conference (WebSci 2020)


8. 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)

2019

1. Privacy Preserving Text Representation Learning. [PDF]
   Ghazaleh Beigi, Kai Shu, Ruocheng Guo, Suhang Wang, and Huan Liu.
   Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT 2019) (Poster paper)


2. Using Synthetic Clickbaits to Improve Prediction and Distinguish between Bot-Generated and Human-Written Headlines. [PDF]
   Thai Le, Kai Shu, Maria D. Molina, Dongwon Lee, S. Shyam Sundar, and Huan Liu.
   Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)
   Media coverage: [Newswise]


3. The Role of User Profiles for Fake News Detection. [PDF]
   Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan Liu.
   Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019) (Short Paper)


4. 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]


5. Exploiting Emojis for Sarcasm Detection. [PDF]
   Jayashree Subramanian*, Varun Sridharan*, Kai Shu, and Huan Liu.
   International Conference on Social Computing, Behavior-Culture Modeling, and Prediction (SBP 2019).


6. 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) .


7. 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]


8. Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items. [PDF]
   Vineeth Rakesh, Suhang Wang, Kai Shu, and Huan Liu.
   Proceedings of 12th ACM International Conference on Web Search and Data Mining (WSDM 2019).

2018

1. 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)


2. Using Social Media to Understand Cyber Attack Behavior. [PDF]
   Amy Sliva, Kai Shu, and Huan Liu.
   9th International Conference on Applied Human Factors and Ergonomics (AHFE 2018)


3. Exploiting User Actions for App Recommendations. [PDF][Slides]
   Kai Shu, Suhang Wang, Jiliang Tang, Yi Chang, Ping Luo, and Huan Liu.
   The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018) (Short Paper)


4. 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).


5. Securing Social Media User Data - An Adversarial Approach. [PDF]
   Ghazaleh Beigi, Kai Shu, Yanchao Zhang, and Huan Liu.
   29th ACM Conference on Hypertext and Social Media (HT 2018).


6. Understanding Cyber Attack Behaviors with Sentiment Information on Social Media. [PDF]
   Kai Shu, Amy Sliva, Justin Sampson, and Huan Liu.
   International Conference on Social Computing, Behavior-Culture Modeling, and Prediction (SBP 2018).


7. Personalized Privacy-Preserving Social Recommendation. [PDF]
   Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, and Yujun Zhang.
   Proceedings of 32nd AAAI Conference on Artificial Intelligence (AAAI 2018).


8. 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).

2017

1. What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation. [PDF]
   Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu.
   Proceedings of 26th International World Wide Web Conference (WWW 2017).

2016

1. Multi-Label Informed Feature Selection. [PDF]
   Ling Jian, Jundong Li, Kai Shu, and Huan Liu.
   Proceedings of 25th International Joint Conference on Artificial Intelligence (IJCAI 2016).

Before 2016

1. Deal or Deceit: Detecting Cheating in Distribution Channels. [PDF][Slides]
   Kai Shu, Ping Luo, Li Wan, Peifeng Yin, and Linpeng Tang.
   Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014).


2. Sequences Modeling and Analysis Based on Complex Network. [PDF]
   Li Wan, Kai Shu, and Yu Guo.
   Communications and Information Processing. Springer Berlin Heidelberg, pages 246-252, 2012.

Workshop, Demo, and Panel Papers

Tutorials  Books  Journals  Conferences

1. Unleashing the Power of Twitter: A Data Analysis of the US Senate’s Social Media Strategies with Unsupervised Machine Learning [PDF]
   Miguel Cozar, Carlos Munoz Losa, and Kai Shu.
   the workshop of LatinX in AI (LXAI) at ICML 2023


2. PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners [PDF]
   Canyu Chen, and Kai Shu.
   the 2nd Workshop on Efficient Natural Language and Speech Processing (ENLSP) at NeurIPS 2022


3. When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes [PDF]
   Canyu Chen, Yueqing Liang, Xiongxiao Xu, Shangyu Xie, Yuan Hong, Kai Shu.
   the workshop on Trustworthy and Socially Responsible Machine Learning (TSRML) at NeurIPS 2022


4. Delving into Data Science Methods in Response to the COVID-19 Infodemic [PDF]
   Miyoung Chong, Chirag Shah, Kai Shu, Jiangen He, Loni Hagen.
   85th Annual Meeting of the Association for Information Science and Technology (ASIS&T 2022)


5. Advances in Social Network Analysis and Mining in the Big Data Era:
   Overview of the IEEE/ACM ASONAM 2021 International Conference. [PDF]
   Michele Coscia, Alfredo Cuzzocrea, and Kai Shu.
   Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2021).


6. Enhancing Model Robustness and Fairness with Causality: A Regularization Approach. [PDF]
   Zhao Wang, Kai Shu, and Aron Culotta.
   Proceedings of the 1st Workshop on Causal Inference & NLP at EMNLP 2021.


7. The Second International MIS2 Workshop: Misinformation and Misbehavior Mining on the Web. [PDF]
   Aude Hofleitner, Meng Jiang, Srijan Kumar, Neil Shah, and Kai Shu.
   Proceedings of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021).


8. Racial Attacks during the COVID-19 Pandemic: Politicizing an Epidemic Crisis on Longstanding Racism and Misinformation, Disinformation, and Misconception. [PDF]
   Miyoung Chong, Thomas J. Froehlich, and Kai Shu
   84th Annual Meeting of the Association for Information Science and Technology (ASIS&T 2021)


9. Toward A Multilingual and Multimodal Data Repository for COVID-19 Disinformation. [PDF]
   Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu
   2020 IEEE International Conference on Big Data (IEEE BigData 2020)


10. Challenges in Combating COVID-19 Infodemic - Data, Tools, and Ethics. [PDF]
    Kaize Ding, Kai Shu, Yichuan Li, Amrita Bhattacharjee and Huan Liu
    29th ACM International Conference on Information and Knowledge Management (CIKM 2020)


11. The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020): Special Edition on Dis/Misinformation Mining from Social Media. [PDF]
    Ebrahim Bagheri, Huan Liu, Kai Shu, and Fattane Zarrinkalam.
    29th ACM International Conference on Information and Knowledge Management (CIKM 2020).


12. dEFEND: An Explainable Fake News Detection System. [PDF][Demo]
    Limeng Cui, Kai Shu, Suhang Wang, Dongwon Lee, and Huan Liu.
    28th ACM International Conference on Information and Knowledge Management (CIKM 2019).


13. Understanding User Profiles on Social Media for Fake News Detection. [PDF]
    Kai Shu, Suhang Wang, and Huan Liu.
    1st IEEE International Workshop on Fake MultiMedia (FakeMM 2018).