Shangbin Feng
Greetings! I am a Ph.D. student at University of Washington, advised by Yulia Tsvetkov. I work on natural language processing, knowledge bases, and social network analysis.
Links: CV Email Twitter Github Google Scholar Semantic Scholar
Shangbin Feng
Greetings! I am a Ph.D. student at University of Washington, advised by Yulia Tsvetkov. I work on natural language processing, knowledge bases, and social network analysis.
Links: CV Email Twitter Github Google Scholar Semantic Scholar
Publications
2023
CooK: Empowering General-Purpose Language Models with Modular and Collaborative Knowledge
Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, Yulia Tsvetkov
arxiv 2023 paper
We propose CooK, a community-driven iniatitive to empower black-box LLMs with modular and collaborative knowledge. By incorporating the outputs of independently trained, small, and specialized LMs, we make LLMs better knowledge models by empowering them with temporal knowledge update, multi-domain knowledge synthesis, and continued improvement through collective efforts.
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang=, Shangbin Feng=, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, Yulia Tsvetkov
Are language models graph reasoners? We propose the NLGraph benchmark, a test bed for graph-based reasoning designed for language models in natural language. We find that LLMs are preliminary graph thinkers while the most advanced graph reasoning tasks remain an open research question.
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Shangbin Feng, Chan Young Park, Yuhan Liu, Yulia Tsvetkov
ACL 2023 🏆 Best Paper Award paper code Washington Post MIT Tech Review
We propose to study the political bias propagation pipeline from pretraining data to language models to downstream tasks. We find that language models do have political biases, such biases are in part picked up from pretraining corpora, and they could result in fairness issues in LM-based solutions to downstream tasks.
FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge
Shangbin Feng, Vidhisha Balachandran, Yuyang Bai, Yulia Tsvetkov
We propose a simple, easy-to-use, shenanigan-free summarization factuality evaluation model by augmenting language models with factual knowledge from knowledge bases.
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding
Shangbin Feng, Zhaoxuan Tan, Wenqian Zhang, Zhenyu Lei, Yulia Tsvetkov
We propose KALM, a Knowledge-Aware Language Model that jointly incorporates external knowledge in three levels of document contexts: local, document-level and global.
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
Zhenyu Lei=, Herun Wan=, Wenqian Zhang, Shangbin Feng, Zilong Chen, Qinghua Zheng, Minnan Luo
We propose to leverage text-graph interaction for Twitter bot detection while modeling semantic consistency in user timelines.
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Yuhan Liu, Zhaoxuan Tan, Heng Wang, Shangbin Feng, Qinghua Zheng, Minnan Luo
We propose community-aware mixture-of-experts to address two challenges in detecting advanced Twitter bots: manipulated features and diverse communities.
BotPercent: Estimating Twitter Bot Populations from Groups to Crowds
Zhaoxuan Tan=, Shangbin Feng=, Melanie Sclar, Herun Wan, Minnan Luo, Yejin Choi, Yulia Tsvetkov
We make the case for community-level bot detection, proposing the system BotPercent to estimate the bot populations from groups to crowds. Armed with BotPercent, we investigate the overall bot percentage among active users, bot precense in the Trump reinstatement vote, and more, yielding numerous interesting findings with implications for social media moderation.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, Minnan Luo
The Web Conference 2023 paper code
We propose to leverage contrastive learning in knowledge graph completion to alleviate the sparsity challenge in existing KGs.
2022
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Minnan Luo
We propose to learn representations of polical actors with social context and expert knowlegde, while applying learned representations to tasks in computational political science.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Shangbin Feng=, Zhaoxuan Tan=, Herun Wan=, Ningnan Wang=, Zilong Chen=, Binchi Zhang=, Qinghua Zheng, Wenqian Zhang, Zhenyu Lei, Shujie Yang, Xinshun Feng, Qingyue Zhang, Hongrui Wang, Yuhan Liu, Yuyang Bai, Heng Wang, Zijian Cai, Yanbo Wang, Lijing Zheng, Zihan Ma, Jundong Li, Minnan Luo (* indicates equal contribution)
NeurIPS 2022, Datasets and Benchmarks Track website paper code poster
We make the case for graph-based Twitter bot detection and propose a graph-based benchmark TwiBot-22, which addresses the issues of limited dataset scale, incomplete graph structure, and low annotation quality in previous datasets.
GraTo: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
Xinshun Feng, Herun Wan, Shangbin Feng, Hongrui Wang, Qinghua Zheng, Jun Zhou, Minnan Luo
We present GraTo, an NAS-GNN framework to tackle over-smoothing in graph mining.
Datavoidant: An AI System for Addressing Political Data Voids on Social Media
Claudia Flores-Saviaga, Shangbin Feng, Saiph Savage
We present Datavoidant, an AI-powered system to combat political data voids within underrepresented communities.
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
Wenqian Zhang=, Shangbin Feng=, Zilong Chen=, Zhenyu Lei, Jundong Li, Minnan Luo (* indicates equal contribution)
NAACL 2022, oral presentation paper code invited talk slides
We introduce the mechanism of knowledge walks to enable multi-hop reasoning on knowledge graphs and levearge textual labels in graphs for political perspective detection.
Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers
Shangbin Feng, Zhaoxuan Tan, Rui Li, Minnan Luo
We introduce relational graph transformers to model relation and influence heterogeneities on Twitter for heterogeneity-aware Twitter bot detection.
2021
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Shangbin Feng=, Zilong Chen=, Wenqian Zhang=, Qingyao Li, Qinghua Zheng, Xiaojun Chang, Minnan Luo (* indicates equal contribution)
We construct a political knowledge graph and propose a graph-based approach for knowledge-aware political perspective detection.
BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks
Shangbin Feng, Herun Wan, Ningnan Wang, Minnan Luo
We propose a graph-based approach for Twitter bot detection with relational graph convolutional networks and four aspects of user information.
TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark
Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, Minnan Luo
CIKM 2021, Resource Track paper code poster
We propose a (the first) comprehensive Twitter bot detection benchmark that covers diversified users and supports graph-based approaches.
SATAR: A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection
Shangbin Feng, Herun Wan, Ningnan Wang, Jundong Li, Minnan Luo
CIKM 2021, Applied Track paper code poster
We propose to pre-train Twitter user representations with follower count and fine-tune on Twitter bot detection.
Miscellaneous