Search Results for author: Kai Shu

Found 50 papers, 22 papers with code

Authorship Attribution for Neural Text Generation

no code implementations EMNLP 2020 Adaku Uchendu, Thai Le, Kai Shu, Dongwon Lee

In recent years, the task of generating realistic short and long texts have made tremendous advancements.

Text Generation

CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection

no code implementations20 Nov 2023 YiFan Li, Zhen Tan, Kai Shu, Zongsheng Cao, Yu Kong, Huan Liu

Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when the data is scarce or imbalanced.

Memorization Representation Learning

Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models

no code implementations15 Nov 2023 Yueqing Liang, Lu Cheng, Ali Payani, Kai Shu

This work investigates the potential of undermining both fairness and detection performance in abusive language detection.

Abusive Language Fairness

Backdoor Activation Attack: Attack Large Language Models using Activation Steering for Safety-Alignment

1 code implementation15 Nov 2023 Haoran Wang, Kai Shu

Our code and data are available at https://github. com/wang2226/Backdoor-Activation-Attack Warning: this paper contains content that can be offensive or upsetting.

Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting

no code implementations16 Oct 2023 Qiong Nan, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Guang Yang, Jintao Li, Kai Shu

To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e. g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts.

Fake News Detection

Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models

1 code implementation8 Oct 2023 Haoran Wang, Kai Shu

While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale.

Claim Verification Decision Making +1

Can LLM-Generated Misinformation Be Detected?

no code implementations25 Sep 2023 Canyu Chen, Kai Shu

Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm.


Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation

1 code implementation15 Sep 2023 Aman Rangapur, Haoran Wang, Kai Shu

Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain.

Explanation Generation Fact Checking +1

Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective

no code implementations6 Sep 2023 Aman Rangapur, Haoran Wang, Kai Shu

In conclusion, this research paper sheds light on the pervasive issue of online financial misinformation and its wide-ranging consequences.


Emulating Reader Behaviors for Fake News Detection

no code implementations27 Jun 2023 Junwei Yin, Min Gao, Kai Shu, Zehua Zhao, Yinqiu Huang, Jia Wang

To this end, we propose an approach of Emulating the behaviors of readers (Ember) for fake news detection on social media, incorporating readers' reading and verificating process to model news from the component perspective thoroughly.

Fake News Detection

MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection

no code implementations18 May 2023 Xiongxiao Xu, Kaize Ding, Canyu Chen, Kai Shu

However, the work exploring limited labeled anomalies and a large amount of unlabeled nodes in graphs to detect anomalies is rather limited.

Graph Anomaly Detection

TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks

1 code implementation17 Apr 2023 Baixiang Huang, Bryan Hooi, Kai Shu

To bridge this gap, we have constructed a real-world graph-based Traffic Accident Prediction (TAP) data repository, along with two representative tasks: accident occurrence prediction and accident severity prediction.

severity prediction

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

1 code implementation24 Dec 2022 Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.

Fake News Detection

Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues

no code implementations10 Nov 2022 Canyu Chen, Haoran Wang, Matthew Shapiro, Yunyu Xiao, Fei Wang, Kai Shu

Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem.


Memory-Guided Multi-View Multi-Domain Fake News Detection

1 code implementation26 Jun 2022 Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, Fuzhen Zhuang

In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.

Fake News Detection

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

2 code implementations21 Jun 2022 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

To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.

Anomaly Detection Benchmarking +2

Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach

no code implementations8 Jun 2022 Yueqing Liang, Canyu Chen, Tian Tian, Kai Shu

Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes.

Classification Decision Making +2

PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners

1 code implementation18 May 2022 Canyu Chen, Kai Shu

Extensive experiment results on few-shot text classification tasks demonstrate the superior performance of the proposed framework by effectively leveraging label semantics and data augmentation for natural language understanding.

Data Augmentation Few-Shot Learning +4

Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo

1 code implementation6 May 2022 Qiang Sheng, Juan Cao, H. Russell Bernard, Kai Shu, Jintao Li, Huan Liu

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.

Domain Adaptive Fake News Detection via Reinforcement Learning

no code implementations16 Feb 2022 Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu, Michelle V. Mancenido, Huan Liu

With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news.

Fake News Detection reinforcement-learning +1

"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders

no code implementations9 Feb 2022 Xinyi Zhou, Kai Shu, Vir V. Phoha, Huan Liu, Reza Zafarani

To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading.

Enhancing Model Robustness and Fairness with Causality: A Regularization Approach

1 code implementation EMNLP (CINLP) 2021 Zhao Wang, Kai Shu, Aron Culotta

In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features.

Causal Inference Fairness

WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding

no code implementations NAACL 2022 Guoqing Zheng, Giannis Karamanolakis, Kai Shu, Ahmed Hassan Awadallah

In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU.

Natural Language Understanding Weakly-supervised Learning

Labeled Data Generation with Inexact Supervision

no code implementations8 Jun 2021 Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang

We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes.


Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features

1 code implementation29 Apr 2021 Tianxiang Zhao, Enyan Dai, Kai Shu, Suhang Wang

Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias.

BIG-bench Machine Learning Fairness

User Preference-aware Fake News Detection

1 code implementation25 Apr 2021 Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

Fact Checking Fake News Detection +2

Fact-Enhanced Synthetic News Generation

1 code implementation8 Dec 2020 Kai Shu, Yichuan Li, Kaize Ding, Huan Liu

The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.

News Generation Text Summarization +1

MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation

2 code implementations8 Nov 2020 Yichuan Li, Bohan Jiang, Kai Shu, Huan Liu

The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two.

Social and Information Networks Computers and Society

Fake News Detection through Graph Comment Advanced Learning

no code implementations3 Nov 2020 Hao Liao, Qixin Liu, Kai Shu, Xing Xie

Yet, the popularity of social media also provides opportunities to better detect fake news.

Fake News Detection Representation Learning Social and Information Networks

Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges

no code implementations18 Oct 2020 Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu

We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.


Combating Disinformation in a Social Media Age

no code implementations14 Jul 2020 Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora Nazer, Kaize Ding, Mansooreh Karami, Huan Liu

The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

1 code implementation23 Jun 2020 Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu

By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.

Classification Drug Discovery +5

Learning with Weak Supervision for Email Intent Detection

no code implementations26 May 2020 Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, Susan Dumais

In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.

intent-classification Intent Classification +2

Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

1 code implementation2 Jan 2020 Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu

In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media.

Ethics Fact Checking

Deep causal representation learning for unsupervised domain adaptation

no code implementations28 Oct 2019 Raha Moraffah, Kai Shu, Adrienne Raglin, Huan Liu

Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.

Representation Learning Unsupervised Domain Adaptation

Detecting Fake News with Weak Social Supervision

no code implementations24 Oct 2019 Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, Huan Liu

This is especially the case for many real-world tasks where large scale annotated examples are either too expensive to acquire or unavailable due to privacy or data access constraints.

Fake News Detection

Applications of Social Media in Hydroinformatics: A Survey

no code implementations1 May 2019 Yu-Feng Yu, Yuelong Zhu, Dingsheng Wan, Qun Zhao, Kai Shu, Huan Liu

Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response. Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structure to better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management.

Fake News Detection Management

The Role of User Profile for Fake News Detection

no code implementations30 Apr 2019 Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, Huan Liu

In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news.

Fake News Detection Feature Importance +1

Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

2 code implementations21 Mar 2019 Kai Shu, Deepak Mahudeswaran, Suhang Wang, Huan Liu

In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection.

Social and Information Networks

Graph Neural Networks for User Identity Linkage

no code implementations6 Mar 2019 Wen Zhang, Kai Shu, Huan Liu, Yalin Wang

In particular, we provide a principled approach to jointly capture local and global information in the user-user social graph and propose the framework {\m}, which jointly learning user representations for user identity linkage.

FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media

7 code implementations5 Sep 2018 Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, Huan Liu

However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information.

Social and Information Networks

Exploiting Tri-Relationship for Fake News Detection

4 code implementations20 Dec 2017 Kai Shu, Suhang Wang, Huan Liu

Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media.

Social and Information Networks

Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications

no code implementations14 Sep 2017 Fred Morstatter, Kai Shu, Suhang Wang, Huan Liu

We apply our solution to sentiment analysis, a task that can benefit from the emoji calibration technique we use in this work.

Sentiment Analysis

Fake News Detection on Social Media: A Data Mining Perspective

6 code implementations7 Aug 2017 Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, Huan Liu

First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.

Fake News Detection

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