Kai Shu

Kai Shu
Illinois Institute of Technology | IIT · Department of Computer Science

Assistant Professor

About

121
Publications
126,098
Reads
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4,725
Citations
Introduction
Additional affiliations
August 2020 - present
Illinois Institute of Technology
Position
  • Professor (Assistant)
Education
September 2015 - July 2020
Arizona State University
Field of study
  • Computer Science

Publications

Publications (121)
Preprint
Full-text available
Machine learning models have demonstrated promising performance in many areas. However, the concerns that they can be biased against specific groups hinder their adoption in high-stake applications. Thus it is essential to ensure fairness in machine learning models. Most of the previous efforts require access to sensitive attributes for mitigating...
Article
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weib...
Preprint
Full-text available
The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of multiple domains. Based on our an...
Preprint
Full-text available
Graph outlier detection is an emerging but crucial machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years, the lack of a standard and unified setting for performance evaluation limits their advancement and usage in real-world applications. To tap the gap, we present, (to our best knowledg...
Preprint
Full-text available
Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to develop effective algori...
Preprint
Full-text available
Recent advances on large pre-trained language models (PLMs) lead impressive gains on natural language understanding (NLU) tasks with task-specific fine-tuning. However, direct fine-tuning PLMs heavily relies on large amount of labeled instances, which are expensive and time-consuming to obtain. Prompt-based tuning on PLMs has proven valuable for fe...
Preprint
Full-text available
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weib...
Article
The current special issue of Neurocomputing was designed to encourage researchers from interdisciplinary domains working on multilingual social media analytics to think beyond the conventional way of combating online hostile posts. The special issue was primarily based on the theme of the First Workshop on Combating On line Ho st ile Posts in Regio...
Preprint
Full-text available
PyGOD is an open-source Python library for detecting outliers on graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for node-, edge-, subgraph-, and graph-level outlier detection, under a unified, well-documented API designed for use by both researchers and practitioners. To overco...
Article
The use of social media has accelerated information sharing and instantaneous communications. The low barrier to enter social media enables more users to participate and makes them stay engaged longer, while incentivizing individuals with a hidden agenda to use disinformation to manipulate information and influence opinions. Disinformation, such as...
Preprint
Full-text available
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. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs. In this work, we address the...
Preprint
Full-text available
Individuals can be misled by fake news and spread it unintentionally without knowing it is false. This phenomenon has been frequently observed but has not been investigated. Our aim in this work is to assess the intent of fake news spreaders. To distinguish between intentional versus unintentional spreading, we study the psychological explanations...
Chapter
Nowadays, social media has become the leading platform for news dissemination and consumption. Due to the convenience of social media platforms, fake news spread at an unprecedented speed, which has brought severe adverse effects to society. In recent years, the method based on deep learning has shown superior performance in fake news detection. Ho...
Article
The proliferation of fake news on social media has the probability to bring an unfavorable impact on public opinion and social development. Many efforts have been paid to develop effective detection and intervention algorithms in recent years. Most of the existing propagation-based fake news detection methods focus on static networks and assume the...
Preprint
Full-text available
Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. 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 c...
Article
Full-text available
The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this st...
Article
Anomaly detection on attributed graphs has received increasing research attention lately due to the broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Heretofore, most of the existing efforts are predominately performed in an unsupervised manner due to the expensive cost of acquiring anomaly labels, e...
Article
Full-text available
The COVID‐19 pandemic crisis has affected everyone's life on a daily basis for more than a year. However, some racial groups have experienced a double pandemic, that of COVID‐19 and racist attacks incorrectly tied to the pandemic. Harassment and physical intimidation were the source of many anti‐Asian attacks. The number of unarmed black people ass...
Article
Full-text available
Background: An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high...
Article
Full-text available
Background: An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high...
Article
Full-text available
The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirty-Fifth Conference on Artificial Intelligence was held virtually from February 8-9, 2021. There were twenty-six workshops in the program: Affective Content Analysis, AI for Behavior Change, AI for Urban Mobility, Artificial Intelligence Safety, Combating O...
Preprint
Full-text available
Building quality machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been shown to provide valuable supervision when large amount of labeled data is unavailable or expensive to obtain. Existing works studying weak supervision for NLU either mostly focus on a specific task or si...
Article
With the rapid increase in access to the internet and the subsequent growth in the population of social media users, the quality of information posted, disseminated, and consumed via these platforms is an issue of growing concern. A large fraction of the common public turn to social media platforms and, in general, the internet for news and even in...
Preprint
Full-text available
BACKGROUND An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. The global scale and high stakes of t...
Preprint
Full-text available
The recent advanced deep learning techniques have shown the promising results in various domains such as computer vision and natural language processing. The success of deep neural networks in supervised learning heavily relies on a large amount of labeled data. However, obtaining labeled data with target labels is often challenging due to various...
Article
Full-text available
Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a lim...
Preprint
Full-text available
Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this causal-inference problem is to identify confounders – variables that cause spurious associations betwe...
Preprint
Full-text available
Though machine learning models are achieving great success, ex-tensive studies have exposed their disadvantage of inheriting latent discrimination and societal bias from the training data, which hinders their adoption on high-state applications. Thus, many efforts have been taken for developing fair machine learning models. Most of them require tha...
Preprint
Full-text available
Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. 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...
Chapter
Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformation and fake news which cause confusion and chaos if not detected in a timely manner. Given the rapidly evolving nature of news events and the limited amount of annotated d...
Chapter
The rapidly increasing number of sharing bikes has facilitated people’s daily commuting significantly. However, the number of available bikes in different stations may be imbalanced due to the free check-in and check-out of users. Therefore, predicting the bike demand in each station is an important task in a city to satisfy requests in different s...
Book
This book constitutes selected and revised papers from the First International Workshop on Combating On line Ho st ile Posts in Regional Languages dur ing Emerge ncy Si tuation, CONSTRAINT 2021, Collocated with AAAI 2021, held as virtual event, in February 2021. The 14 full papers and 9 short papers presented were thoroughly reviewed and selected...
Preprint
Full-text available
The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a new generation method FactGen to generate high-qual...
Preprint
Full-text available
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. Unfortunately, the fake news about COVID-19 is spreading as fast as the virus itself. The incorrect health measurements, anxiety, and hate speeches will have bad consequences on people's physical health,...
Preprint
Disinformation has long been regarded as a severe social problem, where fake news is one of the most representative issues. What is worse, today's highly developed social media makes fake news widely spread at incredible speed, bringing in substantial harm to various aspects of human life. Yet, the popularity of social media also provides opportuni...
Article
Full-text available
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. In this article, we present an overview of the techniques explored to date for the combating of disinfor...
Preprint
Nowadays, there exist powerful language models such as OpenAI's GPT-2 that can generate readable text and can be fine-tuned to generate text for a specific domain. Considering GPT-2, it cannot directly generate synthetic news with respect to a given topic and the output of the language model cannot be explicitly controlled. In this paper, we study...
Preprint
Full-text available
With the rapid increase in access to internet and the subsequent growth in the population of online social media users, the quality of information posted, disseminated and consumed via these platforms is an issue of growing concern. A large fraction of the common public turn to social media platforms and in general the internet for news and even in...
Conference Paper
Full-text available
Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods are supervised and, thus, have two key drawbacks: (1) The data labeli...
Preprint
Full-text available
Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods are supervised and, thus, have two key drawbacks: (1) The data labeli...
Conference Paper
Full-text available
Email remains one of the most frequently used means of online communication. People spend significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to r...
Preprint
Full-text available
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. In this paper, we present an overview of the techniques explored to date for the combating of disinforma...
Preprint
Full-text available
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of nod...
Chapter
Full-text available
In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media. The wide spread of disinformation and fake news can cause detrimental societal effects. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, d...
Article
Full-text available
Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of fake news, that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received...
Article
Full-text available
Limited labeled data is becoming one of the largest bottlenecks for supervised learning systems. This is especially the case for many real-world tasks where large scale labeled examples are either too expensive to acquire or unavailable due to privacy or data access constraints. Weak supervision has shown to be effective in mitigating the scarcity...
Preprint
Full-text available
While the COVID-19 pandemic continues its global devastation, numerous accompanying challenges emerge. One important challenge we face is to efficiently and effectively use recently gathered data and find computational tools to combat the COVID-19 infodemic, a typical information overloading problem. Novel coronavirus presents many questions withou...