Commission on Information Disorder Final Report

America is in a crisis of trust and truth. Bad information has become as prevalent, persuasive, and persistent as good information, creating a chain reaction of harm. It makes any health crisis more deadly. It slows down response time on climate change. It undermines democracy.The Aspen Institute's Commission on Information Disorder was created to address these conditions. Co-chaired by award-winning journalist Katie Couric, cybersecurity expert Chris Krebs, and civil rights leader Rashad Robinson, the Commission is composed of a diverse group from across the political spectrum, representing academia, government, philanthropy, and civil society. Over the course of six months, commissioners held internal discussions and heard from experts, community leaders, academics, researchers, tech industry representatives, and lawmakers to understand and explore the multidimensional attributes of information disorder.The Commission's Final Report is the culmination of that in-depth investigation. Offering a viable framework for action, it makes 15 recommendations for how government, private industry, and civil society can help to increase transparency and understanding, build trust, and reduce harms

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

The Impact of Anthropologically Motivated Human Social Networks on the Transmission Dynamics of Infectious Disease

abstract: Understanding the consequences of changes in social networks is an important an- thropological research goal. This dissertation looks at the role of data-driven social networks on infectious disease transmission and evolution. The dissertation has two projects. The first project is an examination of the effects of the superspreading phenomenon, wherein a relatively few individuals are responsible for a dispropor- tionate number of secondary cases, on the patterns of an infectious disease. The second project examines the timing of the initial introduction of tuberculosis (TB) to the human population. The results suggest that TB has a long evolutionary history with hunter-gatherers. Both of these projects demonstrate the consequences of social networks for infectious disease transmission and evolution. The introductory chapter provides a review of social network-based studies in an- thropology and epidemiology. Particular emphasis is paid to the concept and models of superspreading and why to consider it, as this is central to the discussion in chapter 2. The introductory chapter also reviews relevant epidemic mathematical modeling studies. In chapter 2, social networks are connected with superspreading events, followed by an investigation of how social networks can provide greater understanding of in- fectious disease transmission through mathematical models. Using the example of SARS, the research shows how heterogeneity in transmission rate impacts super- spreading which, in turn, can change epidemiological inference on model parameters for an epidemic. Chapter 3 uses a different mathematical model to investigate the evolution of TB in hunter-gatherers. The underlying question is the timing of the introduction of TB to the human population. Chapter 3 finds that TB’s long latent period is consistent with the evolutionary pressure which would be exerted by transmission on a hunter- igatherer social network. Evidence of a long coevolution with humans indicates an early introduction of TB to the human population. Both of the projects in this dissertation are demonstrations of the impact of var- ious characteristics and types of social networks on infectious disease transmission dynamics. The projects together force epidemiologists to think about networks and their context in nontraditional ways.Dissertation/ThesisDoctoral Dissertation Anthropology 201