Misinformation Detection in Social Media

abstract: The pervasive use of social media gives it a crucial role in helping the public perceive reliable information. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creation and dissemination of misinformation. It becomes increasingly difficult for online users to find accurate and trustworthy information. As witnessed in recent incidents of misinformation, it escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Different from some existing research in psychology and social sciences about misinformation, social media platforms pose unprecedented challenges for misinformation detection. First, intentional spreaders of misinformation will actively disguise themselves. Second, content of misinformation may be manipulated to avoid being detected, while abundant contextual information may play a vital role in detecting it. Third, not only accuracy, earliness of a detection method is also important in containing misinformation from being viral. Fourth, social media platforms have been used as a fundamental data source for various disciplines, and these research may have been conducted in the presence of misinformation. To tackle the challenges, we focus on developing machine learning algorithms that are robust to adversarial manipulation and data scarcity. The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field

Understanding Bots on Social Media - An Application in Disaster Response

abstract: Social media has become a primary platform for real-time information sharing among users. News on social media spreads faster than traditional outlets and millions of users turn to this platform to receive the latest updates on major events especially disasters. Social media bridges the gap between the people who are affected by disasters, volunteers who offer contributions, and first responders. On the other hand, social media is a fertile ground for malicious users who purposefully disturb the relief processes facilitated on social media. These malicious users take advantage of social bots to overrun social media posts with fake images, rumors, and false information. This process causes distress and prevents actionable information from reaching the affected people. Social bots are automated accounts that are controlled by a malicious user and these bots have become prevalent on social media in recent years. In spite of existing efforts towards understanding and removing bots on social media, there are at least two drawbacks associated with the current bot detection algorithms: general-purpose bot detection methods are designed to be conservative and not label a user as a bot unless the algorithm is highly confident and they overlook the effect of users who are manipulated by bots and (unintentionally) spread their content. This study is trifold. First, I design a Machine Learning model that uses content and context of social media posts to detect actionable ones among them; it specifically focuses on tweets in which people ask for help after major disasters. Second, I focus on bots who can be a facilitator of malicious content spreading during disasters. I propose two methods for detecting bots on social media with a focus on the recall of the detection. Third, I study the characteristics of users who spread the content of malicious actors. These features have the potential to improve methods that detect malicious content such as fake news.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Graph learning for anomaly analytics : algorithms, applications, and challenges

Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery

Decision making and creativity: A qualitative study of MacArthur fellows

This research study explored how eight individuals recognized for their creativity activate, develop, express, and sustain their creativity through decision making. The individuals were MacArthur Fellowship award winners. This prestigious fellowship is given to individuals who the MacArthur Foundation considers to be high-achieving and highly innovative individuals. The Fellowship recipients in this study were affiliated with either nonprofit or for-profit organizations, and all were founders of their respective organizations. The specific goals of the research were to: (a) understand the details of participant decision making strategies and processes; (b) investigate if participants demonstrate consistent or different decision making strategies across the sample and across different decision making contexts; (c) compare the strategies and processes of participants with the established theories of decision making; and (d) understand how the creative thinkers activate, develop, express, and sustain their creativity in their pursuit of novel outcomes. This was a qualitative study that employed face-to-face interviewing as the primary data collection method. Participants were chosen using a purposeful sampling technique in which potential participants were stratified by gender, age, and organizational type and then randomly selected from each category. Interviewees came from different regions of the United States and worked in a range of fields including physics, agriculture, computer technology, human rights, conservation, pharmaceuticals, environmental policy, and music. An interview guide was employed to give interviews structure and maximize the busy interviewees’ time. Interviews lasted approximately 60 minutes. Interview data were organized into single case studies built around constructs that surfaced during a review of the literature on both decision making and creativity. A cross-case analysis was also conducted. The results of the study supported existing theories of decision making, though these theories are relatively abstract and this study presents richer descriptions of the decision making process than one can find in the more abstract theoretical literature. As a consequence, this study should be useful to those who want to emulate individuals who have been publicly recognized for their creativity and for successfully making decisions in areas where well-established decision making pathways do not exist

Technological Innovation, Data Analytics, and Environmental Enforcement

Technical innovation is ubiquitous in contemporary society and contributes to its extraordinarily dynamic character. Sometimes these innovations have significant effects on the state of the environment or on human health and they have stimulated efforts to develop second order technologies to ameliorate those effects. The development of the automobile and its impact on life in the United States and throughout the world is an example. The story of modern environmental regulation more generally includes chapters filled with examples of similar efforts to respond to an enormous array of technological advances. This Article uses a different lens to consider the role of technological innovation. In particular, it considers how technological advances have the potential to shape governance efforts in the compliance realm. The Article demonstrates that such technological advances – especially new and improved monitoring capacity, advances in information dissemination through e-reporting and other techniques, and improved capacity to analyze information – have significant potential to transform governance efforts to promote compliance. Such transformation is likely to affect not only the “how” of compliance promotion, but also the “who.” Technological innovation is likely to contribute to new thinking about the roles key actors can and should play in promoting compliance with legal norms. The Article discusses some of the potential benefits of these types of technological innovation in the context of the Environmental Protection Agency (EPA)’s ongoing efforts to improve its compliance efforts by taking advantage of emerging technologies. We also identify some of the pitfalls or challenges that agencies such as EPA need to be aware of in opening this emerging bundle of new tools and making use of them to address real-world environmental needs

Technological Innovation, Data Analytics, and Environmental Enforcement

Technical innovation is ubiquitous in contemporary society and contributes to its extraordinarily dynamic character. Sometimes these innovations have significant effects on the state of the environment or on human health and they have stimulated efforts to develop second order technologies to ameliorate those effects. The development of the automobile and its impact on life in the United States and throughout the world is an example. The story of modern environmental regulation more generally includes chapters filled with examples of similar efforts to respond to an enormous array of technological advances. This Article uses a different lens to consider the role of technological innovation. In particular, it considers how technological advances have the potential to shape governance efforts in the compliance realm. The Article demonstrates that such technological advances – especially new and improved monitoring capacity, advances in information dissemination through e-reporting and other techniques, and improved capacity to analyze information – have significant potential to transform governance efforts to promote compliance. Such transformation is likely to affect not only the “how” of compliance promotion, but also the “who.” Technological innovation is likely to contribute to new thinking about the roles key actors can and should play in promoting compliance with legal norms. The Article discusses some of the potential benefits of these types of technological innovation in the context of the Environmental Protection Agency (EPA)’s ongoing efforts to improve its compliance efforts by taking advantage of emerging technologies. We also identify some of the pitfalls or challenges that agencies such as EPA need to be aware of in opening this emerging bundle of new tools and making use of them to address real-world environmental needs