Data Driven Inference in Populations of Agents

abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive.  More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.   This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Are We All in a Truman Show? Spotting Instagram Crowdturfing through Self-Training

Influencer Marketing generated $16 billion in 2022. Usually, the more popular influencers are paid more for their collaborations. Thus, many services were created to boost profiles' popularity metrics through bots or fake accounts. However, real people recently started participating in such boosting activities using their real accounts for monetary rewards, generating ungenuine content that is extremely difficult to detect. To date, no works have attempted to detect this new phenomenon, known as crowdturfing (CT), on Instagram. In this work, we propose the first Instagram CT engagement detector. Our algorithm leverages profiles' characteristics through semi-supervised learning to spot accounts involved in CT activities. Compared to the supervised approaches used so far to identify fake accounts, semi-supervised models can exploit huge quantities of unlabeled data to increase performance. We purchased and studied 1293 CT profiles from 11 providers to build our self-training classifier, which reached 95\% F1-score. We tested our model in the wild by detecting and analyzing CT engagement from 20 mega-influencers (i.e., with more than one million followers), and discovered that more than 20% was artificial. We analyzed the CT profiles and comments, showing that it is difficult to detect these activities based solely on their generated content

Whose Honey, Whose Hive?: Genre and Rhetorical Agency in the U.S. Colony Collapse Disorder

This dissertation analyzes the rhetoric surrounding the environmental crisis of the honey bee Colony Collapse Disorder, commonly known as CCD. Since 2007, the United States has lost on average a third of its honey bee colonies each year to CCD. The crisis has potentially serious environmental consequences. Without honey bee pollination services, over $14 billion worth of crops in the United States alone are in jeopardy. Drawing on environmental rhetoric, genre theory, and agricultural rhetorics, I offer a rhetorical analysis and genre analysis of the narratives surrounding CCD from select popular press newspaper articles, documentaries, nonfiction works, and personal interviews with beekeepers that cover the span of the early years of the U.S. crisis from 2007 to 2011. I argue that specific narratives of CCD offered by stakeholders such as scientists, reporters, beekeepers, policymakers, and environmentalists both constrained and invited deliberation about the synergistic causes of the crisis. One narrative I examine in detail in Chapter Two is the nesting genre of the “crime mystery” of CCD in news stories that often reduced consideration of the causes of CCD to a warring search for a pathogenic solution. This focus on a “smoking gun” for CCD focused the public’s attention on scientists seeking a single solution instead of considering multi-factoral causes. The genre also reduced consideration of the multiple roles stakeholders played in the crisis. In contrast, beekeepers’ protests, insights and perspectives (Chapters Three and Four) and the trope “listening to bees” popular in nonfiction media (Chapter Five) expanded consideration of systemic economic and cultural causes for the crisis, and allowed bees and beekeepers to emerge as informative agents. This project considers, too, how American beekeepers have approached CCD in largely individualistic terms in contrast to French beekeepers who have collectively organized in large groups to protest their sense that CCD was caused by the sale of a pesticide by the Bayer Corporation. I apply rhetorical and genre analysis to representations of CCD in popular media and beekeepers’ discourse. I cite stakeholders such as scientists, researchers, journalists, beekeepers, and protestors. This dissertation contributes to scholarship in environmental rhetoric and environmental communication that analyzes the narratives and causes of environmental crises. This project evaluates the solutions and challenges that varied stakeholders have posed, specifically through analyzing the shaping and impact of their narratives. Ultimately, the concluding chapter argues for the trope of “listening to bees,” the idea that bees are a critical indicator species whose behavior informs how we should approach and potentially solve this crisis

Can policy be risk-based? The cultural theory of risk and the case of livestock disease containment

This article explores the nature of calls for risk-based policy present in expert discourse from a cultural theory perspective. Semi-structured interviews with professionals engaged in the research and management of livestock disease control provide the data for a reading proposing that the real basis of policy relating to socio-technical hazards is deeply political and cannot be purified through ‘escape routes’ to objectivity. Scientists and risk managers are shown calling, on the one hand, for risk-based policy approaches while on the other acknowledging a range of policy drivers outside the scope of conventional quantitative risk analysis including group interests, eventualities such as outbreaks, historical antecedents, emergent scientific advances and other contingencies. Calls for risk-based policy are presented, following cultural theory, as ideals connected to a reductionist epistemology and serving particular professional interests over others rather than as realistic proposals for a paradigm shift

Young People, Social Media and Health

The pervasiveness of social media in young people’s lives is widely acknowledged, yet there is little evidence-based understanding of the impacts of social media on young people’s health and wellbeing. Young People, Social Media and Health draws on novel research to understand, explain, and illustrate young people’s experiences of engagement with health-related social media; as well as the impacts they report on their health, wellbeing, and physical activity. Using empirical case studies, digital representations, and evidence from multi-sector and interdisciplinary stakeholders and academics, this volume identifies the opportunities and risk-related impacts of social media. Offering new theoretical insights and practical guidelines for educators, practitioners, parents/guardians, and policy makers; Young People, Social Media and Health will also appeal to students and researchers interested in fields such as Sociology of Sport, Youth Sports Development, Secondary Physical Education, and Media Effects