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Dongwon Lee Professor (Ph.D., UCLA) and Director of Doctoral Programs College of Information Sciences and Technology (IST) Penn State University (PSU), USA

In general, I am interested in the research issues involving the management of and mining in data in diverse forms (e.g., structured records, text, multimedia, social media, and Web), and their social implications or security applications. People seem to call such as (part of) Data Science, Social Computing, and/or Cybersecurity these days. Here is my Google Scholar Profile and my ORCID. More details can be found here: my research group (PIKE) and the full publication list. Selected publications in recent 5 years are listed below:

• Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation, EMNLP 2023
• MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark, EMNLP 2023
• Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?, HCOMP 2023
• Information Operations in Turkey: Manufacturing Resilience with Free Twitter Accounts, ICWSM 2023
• Associative Inference Can Increase People’s Susceptibility to Misinformation, ICWSM 2023
• Do Language Models Plagiarize?, WWW 2023
• LANCER: A Lifetime-Aware News Recommender System, AAAI 2023
• WARNER: Weakly-Supervised Neural Network to Identify Eviction Filing Hotspots in the Absence of Court Records, CIKM 2022
• Forecasting the Number of Tenants At-Risk of Formal Eviction: A Machine Learning Approach to Inform Public Policy, IJCAI 2022
• KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums, SIGIR 2022
• If You Have a Reliable Source, Say Something: Effects of Correction Comments on COVID-19 Misinformation, ICWSM 2022
• SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher, ACL 2022
• CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents, AAMAS 2022
• Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning, WWW 2022
• ALLIE: Active Learning on Large-scale Imbalanced Graphs, WWW 2022
• Not All Layers Are Equal: A Layer-Wise Adaptive Approach Toward Large-Scale DNN Training, WWW 2022
• MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems, ICDM 2021
• ALADDIN: Asymmetric Centralized Training for Distributed Deep Learning, CIKM 2021
• ChamberBreaker: Mitigating the Echo Chamber Effect and Supporting Information Hygiene through a Gamified Inoculation System, CSCW 2021
• Large-Scale Data-Driven Airline Market Influence Maximization, KDD 2021
• A PLAN for Tackling the Locust Crisis in East Africa: Harnessing Spatiotemporal Deep Models for Locust Movement Forecasting, KDD 2021
• A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks, ACL 2021
• Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks, SIGIR 2021
• MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories, NAACL 2021
• Does Clickbait Actually Attract More Clicks? Three Clickbait Studies You Must Read, CHI 2021
• WILSON: A Divide and Conquer Approach for Fast and Effective News Timeline Summarization, EDBT 2021
• Authorship Attribution for Neural Text Generation, EMNLP 2020
• MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models, ICDM 2020
• TOMATO: A Topic-Wise Multi-Task Sparsity Model, CIKM 2020
• GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction, KDD 2020
• DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation, KDD 2020
• Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers, KDD 2020
• Gatekeeper: Analyzing G-Indexes and Improving Service Quantification, JCDL 2020
• 5 Sources of Clickbaits You Should Know! Using Synthetic Clickbaits to Improve Prediction and Distinguish between Bot-Generated and Human-Written Headlines, ASONAM 2019
• SAME: Sentiment-Aware Multi-Modal Embedding for Detecting Fake News, ASONAM 2019
• dEFEND: Explainable Fake News Detection, KDD 2019
• Trust It or Not: Effects of Machine Learning Warning in Helping Individuals Mitigate Misinformation, WebSci 2019
• Constrained Local Graph Clustering by Colored Random Walk, WWW 2019
• l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items, TKDE 2019