This study partially replicated the research of Cook, Kwak, Hoffman, & Loftus where they examined post-event activities that induces subjects to pick a wrong person in a forced choice identification procedure. The goal was to investigate if providing a neither option to a match to sample task increases the accuracy of responding. Subjects were asked to study three faces for 10 seconds, after which they were asked to pick out the faces in a forced choice setting where two other faces were presented. Later the subjects were asked to pick out faces in a setting in which they could use a neither option. Results indicated that a generalization effect occurs when identifying faces and the effect is seen as subjects choosing the wrong face. This suggests that when using faces with some similar features in a lineup setting the procedure may cause the subject to pick the wrong person.

The rate of development for large-scale wind energy in the Canadian province of Saskatchewan is a complex issue such that the various actors of the surrounding policy community (decision-makers, influential stakeholders, and the attentive public) cannot reach consensus. Inaction on resource and environmental issues like this one is often the result of complexity, either the inherent complexity of the problem being targeted or the complexity of the communicated information surrounding the problem. Inherent complexity is managed chiefly by central decision-makers and influential stakeholders of the policy community, while information complexity must be dealt with primarily by the attentive public of the policy community. This thesis uses a case study of large-scale wind energy development in Saskatchewan to explore complexity as a root cause of environmental inaction. In manuscript style, this thesis investigates two types of environmental complexity and two segments of the wind energy policy community. Through an exploration of barriers to wind energy expansion in Saskatchewan, the first manuscript focuses on the complexity of environmental problems themselves as dealt with by decision-makers and other influential policy actors. Interviews were conducted with a range of experts and stakeholders where participants were asked to describe barriers to development in each of six categories: agreement, knowledge, technology, economic, social, and political barriers. A number of key issues are identified: disagreement regarding the balance between environment and economy, contradictory knowledge about the benefits of wind energy, conflicting faith in technology to accommodate high levels of wind energy, unquantified non-economic benefits of wind energy, lack of social interest in and support for wind energy, and lagging provincial political leadership on the issue of wind energy. Perhaps more importantly, the interviews reveal that experts disagreed on many facets of the wind energy issue, which demonstrates that the complexity of the issue makes consensus and any resulting action difficult to accomplish. Intuitive solutions for managing complexity through the more effective reconciliation of disagreement are also suggested. The second manuscript focuses on the complexity of environmental information by examining policy information regarding wind energy implementation in Saskatchewan for complications that might reduce understanding about and participation in the issue by the attentive public. Through a review of publicly available reports, articles, and documents, four complexity-related problems are uncovered: non-intuitive information, misreported information, obsolete information, and absent information. Such occurrences may well be problematic for environmental policy information in general, so intuitive solutions involving clarification and elaboration are suggested for managing each one. Together, the two manuscripts illustrate that both inherent and information complexity can be problems for environmental issues, especially when one causes or feeds back into the other. Results from this thesis provide a way of thinking about environmental complexity and understanding environmental inaction as managed by policy communities.

The rate of development for large-scale wind energy in the Canadian province of Saskatchewan is a complex issue such that the various actors of the surrounding policy community (decision-makers, influential stakeholders, and the attentive public) cannot reach consensus. Inaction on resource and environmental issues like this one is often the result of complexity, either the inherent complexity of the problem being targeted or the complexity of the communicated information surrounding the problem. Inherent complexity is managed chiefly by central decision-makers and influential stakeholders of the policy community, while information complexity must be dealt with primarily by the attentive public of the policy community. This thesis uses a case study of large-scale wind energy development in Saskatchewan to explore complexity as a root cause of environmental inaction. In manuscript style, this thesis investigates two types of environmental complexity and two segments of the wind energy policy community. Through an exploration of barriers to wind energy expansion in Saskatchewan, the first manuscript focuses on the complexity of environmental problems themselves as dealt with by decision-makers and other influential policy actors. Interviews were conducted with a range of experts and stakeholders where participants were asked to describe barriers to development in each of six categories: agreement, knowledge, technology, economic, social, and political barriers. A number of key issues are identified: disagreement regarding the balance between environment and economy, contradictory knowledge about the benefits of wind energy, conflicting faith in technology to accommodate high levels of wind energy, unquantified non-economic benefits of wind energy, lack of social interest in and support for wind energy, and lagging provincial political leadership on the issue of wind energy. Perhaps more importantly, the interviews reveal that experts disagreed on many facets of the wind energy issue, which demonstrates that the complexity of the issue makes consensus and any resulting action difficult to accomplish. Intuitive solutions for managing complexity through the more effective reconciliation of disagreement are also suggested. The second manuscript focuses on the complexity of environmental information by examining policy information regarding wind energy implementation in Saskatchewan for complications that might reduce understanding about and participation in the issue by the attentive public. Through a review of publicly available reports, articles, and documents, four complexity-related problems are uncovered: non-intuitive information, misreported information, obsolete information, and absent information. Such occurrences may well be problematic for environmental policy information in general, so intuitive solutions involving clarification and elaboration are suggested for managing each one. Together, the two manuscripts illustrate that both inherent and information complexity can be problems for environmental issues, especially when one causes or feeds back into the other. Results from this thesis provide a way of thinking about environmental complexity and understanding environmental inaction as managed by policy communities.

This thesis examines the Checkpoint research project and verification initiative that was introduced to address misinformation in private messaging applications during the 2019 Indian general election. Over two months, throughout the seven phases of the election, a team of analysts verified election related misinformation spread on the closed messaging network WhatsApp. Building on new automated technology, the project introduced a WhatsApp tipline which allowed users of the application to submit content to a team of analysts that verified user-generated content in an unprecedented way. The thesis presents a detailed ethnographic account of the implementation of the verification project. Ethnographic fieldwork has been combined with a series of semi-structured interviews in which analysts are underlining the challenges they faced throughout the project. Among the challenges, this study found that India’s legal framework limited the scope of the project so that the organisers had to change approach from an editorial project to one that was research based. Another problem touched the methodology of verification. Analysts perceived the use of online verification tools as a limiting factor when verifying content, as they experienced a need for more traditional journalistic verification methods. Technology was also a limiting factor. The tipline was quickly flooded with verification requests, the majority of which were unverifiable, and the team had to sort the queries manually. Existing technology such as image match check could be further implemented to deal more efficiently with multiple queries in future projects.

With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that can find checkworthy information, detect fallacious argumentation of online content, retrieve relevant evidence from authoritative sources and analyze the veracity of claims given the retrieved evidence. The robustness and applicability of these systems depend on the availability of annotated resources to train machine learning models in a supervised fashion, as well as machine learning models that capture patterns beyond domain-specific lexical clues or genre-specific stylistic insights. In this thesis, we investigate the role of models for argument structure and argument quality in improving tasks relevant to fact-checking and furthering our understanding of misinformation and disinformation. We contribute to argumentation mining, misinformation detection, and fact-checking by releasing multiple annotated datasets, developing unified models across datasets and task formulations, and analyzing the vulnerabilities of such models in adversarial settings. We start by studying the argument structure's role in two downstream tasks related to fact-checking. As it is essential to differentiate factual knowledge from opinionated text, we develop a model for detecting the type of news articles (factual or opinionated) using highly transferable argumentation-based features. We also show the potential of argumentation features to predict the checkworthiness of information in news articles and provide the first multi-layer annotated corpus for argumentation and fact-checking. We then study qualitative aspects of arguments through models for fallacy recognition. To understand the reasoning behind checkworthiness and the relation of argumentative fallacies to fake content, we develop an annotation scheme of fallacies in fact-checked content and investigate avenues for automating the detection of such fallacies considering single- and multi-dataset training. Using instruction-based prompting, we introduce a unified model for recognizing twenty-eight fallacies across five fallacy datasets. We also use this model to explain the checkworthiness of statements in two domains. Next, we show our models for end-to-end fact-checking of statements that include finding the relevant evidence document and sentence from a collection of documents and then predicting the veracity of the given statements using the retrieved evidence. We also analyze the robustness of end-to-end fact extraction and verification by generating adversarial statements and addressing areas for improvements for models under adversarial attacks. Finally, we show that evidence-based verification is essential for fine-grained claim verification by modeling the human-provided justifications with the gold veracity labels.

In a large body of research, Elizabeth Loftus (1975) first illuminated major concerns about the inaccuracy of eyewitness accounts. The primary goal of the present research was to test whether training regarding common eyewitness mistakes and witness suggestibility could improve eyewitness accuracy. The experimental group watched a presentation on research conducted by Elizabeth Loftus (1975) on eyewitness testimony and suggestibility during a Psychology course. Afterwards, an actor interrupted the classroom and had a discussion with the teacher. Students were asked a series of questions about the disruption. Some of the questions were leading and suggested certain things about the disruption that were inaccurate. After the misleading questions were asked, students were instructed to write a brief summary of what they saw. One week later, the students were asked direct questions about the disruption. A control group did not receive the presentation on eyewitness testimony, but witnessed the exact same event as the experimental group and followed the same procedure. The results suggest that participants who were trained were not as influenced as participants in the control group. Additionally, students in the control group reported the actor's behavior as more threatening than did the experimental group. This research not only adds to the existing literature, but has the potential to improve current eyewitness identification procedures in order to strengthen our justice system.

The goal of this thesis is to explore the various forms of rhetoric utilized in digital communities pertaining to COVID-19. The body of this thesis synthesizes social media data with original human subjects research, supplemented by a review of the literature surrounding digital communication. The analysis of these freestanding communities highlights the differences in communication throughout these spaces, as well as discusses their differences in reaction to disordered information. Through rhetorical analysis of the language employed by COVID-19 denialist communities on Twitter and a review of the experiences of COVID-19 “long-haulers” in COVID-19 related online communities (such as Facebook and Reddit), this project offers novel insights into COVID-19 communication and the spread of misinformation.

With the explosion of social media, consumers are gaining control in social reach and can utilize online platforms to create and share misleading information when doing so helps to meet an end. This dissertation, consisting of three separate essays, represents an attempt to address how misleading information is created, how it is disseminated, and how it can be eliminated. Essay One (Chapter 2) uses a mixed-method approach to explore the Dark Triad, proactivity, and vigilantism in driving self-created misleading information sharing. Additionally, this essay introduces a dual-process model of inoculation theory to the marketing and consumer literature that shows how consumers autoinoculate when building justification to engage in malicious behavior. This process includes both automatic and analytical components that initiate a Negative Cascade. Without a larger number of posts, these initial messages may be overlooked. However, herd inoculation can develop when a message begins to sway larger groups. Essay Two (Chapter 3) determines that authentic messages from the original poster are most believable and most likely to initiate a Negative Cascade. This confirmation through mere exposure can then initiate herd inoculation as it flows to other consumers and develops further credibility. The implicit bystander effect is active when in the presence of larger groups. Findings suggest herd inoculation may go unbroken since posters exposed to a positive counter-cascade are less likely to both participate in a forum and post positive messages. Essay Three (Chapter 4) shows that when a consumer shares a message that develops into a Negative Cascade, additional effort is required to halt the consumer herd inoculation. The studies uncover the need for an overt response from the original poster to stop future sharing of misleading information and the role of brand-enacted quarantines in the prevention of the autoinoculation of consumer vigilantes. This dissertation shows how one message can become a much bigger problem for a brand when misinformation spreads. Insights within the dissertation provide numerous outlets for future research and numerous tools and recommendations for both academics and practitioners that hope to understand how misleading information is created, disseminated, and can be eliminated.

No description available.

In the wake of events like the COVID-19 pandemic, the storming of the Capitol, and the Russian invasion of Ukraine, it’s time to start labeling misinformation for what it is: a negative externality to society. The spillover effects from the proliferation of mis- and disinformation have the potential to negatively impact the institution of democracy, civic engagement, and downstream health outcomes. Put simply, to understand the misinformation problem is to understand its complexities, its pitfalls, and its motivations. Taken as a whole, this paper articulates the need for a divergence from conventional economic theory on efficiency to a pro-social, welfare-based approach to internalization efforts. In doing so, this analysis presents a full-scale characterization of misinformation as a negative externality, starting with the reorganization of traditional microeconomic theory, followed by a platform-by-platform evaluation of various internalization strategies and evidence from the literature on the impacts of misinformation, and concluding with a commentary on potential remediation approaches. / Thesis ( BA ) — Boston College, 2022 . / Submitted to: Boston College. College of Arts and Sciences . / Discipline: Departmental Honors . / Discipline: Economics .