Tag: Climate Change Disinformation

Abstract

During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data. In this study, we propose a Hierarchical Graph-based Integration Network (H-GIN) designed for detecting propaganda in text within a defined domain using multilabel classification. H-GIN is extracted to build a bi-layer graph inter-intra-channel, such as Residual-driven Enhancement and Processing (RDEP) and Attention-driven Multichannel feature Fusing (ADMF) with suitable labels at two distinct classification levels. First, RDEP procedures facilitate information interactions between distant nodes. Second, by employing these guidelines, ADMF standardizes the Tri-Channels 3-S (sequence, semantic, and syntactic) layer, enabling effective propaganda detection through related and unrelated information propagation of news representations into a classifier from the existing ProText, Qprop, and PTC datasets, thereby ensuring its availability to the public. The H-GIN model demonstrated exceptional performance, achieving an impressive 82% accuracy and surpassing current leading models. Notably, the model’s capacity to identify previously unseen examples across diverse openness scenarios at 82% accuracy using the ProText dataset was particularly significant.

Abstract

Riverine flooding is increasing in frequency and intensity, requiring river management agencies to consider new approaches to working with communities on flood mitigation planning. Communication and information sharing between agencies and communities is complex, and mistrust and misinformation arise quickly when communities perceive that they are excluded from planning. Subsequently, riverfront community members create narratives that can be examined as truth regimes—truths created and repeated that indicate how flooding and its causes are understood, represented, and discussed within their communities—to explain why flooding occurs in their area. To better understand community perceptions of river management related to repeated flooding, we employed a qualitative methodology of semi-structured interviews with 112 community members in 3 communities on the Missouri River, USA. Discourse analysis of the interviews revealed three dominant truth regimes that shape perceptions of river management in these communities: (1) upstream reservoir releases are driven by recreational aims, such as fishing and boating within reservoirs, instead of downstream flood control; (2) endangered species protection surpasses other river values and flood management; and (3) river navigation for commerce is no longer prioritized. For environmental managers, understanding the truth regimes circulating within local affected communities can help moderate mistrust of and frustration with governing bodies, guide project messaging to disarm false truth regimes, and improve the communication of river science, management options and policy implementation.

Abstract

Environmental harms that disproportionately impact marginalized communities call for collective action. Yet mobilizing across diverse communities poses challenges. This qualitative study explored how a Baltimore-based community organization attempted to build power and mobilize around environmental concerns with the aim of uniting communities across racial, spatial, and socioeconomic divides. Drawing on qualitative interviews (N = 39) with organizers and community members, we examined (1) how participants understand and frame environmental harms and (2) the interplay between environmental harms and community organizing processes. Findings revealed three themes. First, participants framed environmental harms as both bounded (concentrated in neighborhoods) and unbounded (transcending geographic boundaries). A human rights framework helped bridge understandings of seemingly disparate environmental injustices. Second, organizational processes characterized by fluidity enabled shifting roles, issue areas, and conceptual framings as contexts changed. Finally, nurturing community capacity through attending to power imbalances, skill building, and connecting environmental harms and human rights further enabled organizing. The advocacy efforts achieved some of its goals, while the organizing processes developed participants and community consciousness, leadership, and solidarity across differences. The study suggests intentional framing of environmental concerns through a human rights lens, flexible organizing processes, and purposeful capacity-building can nurture the solidarity and sustained commitment required for communities to mobilize power and address environmental injustices, even amidst diversity. Measuring success should extend beyond achievement of specific environmental goals to also account for lasting impacts on individuals and communities engaged in this work.

Abstract

The prospect of artificial intelligence (AI) matching and surpassing human intelligence continues to intrigue. On the foundations of the exceptional advances in AI technologies in the last decade, the potential for competitive advantages makes AI in general and Generative AI in particular one of the most promising technologies for marketing. However, while there are robust theoretical advances in the domain of AI for marketing, how AI impacts marketing entities is poorly understood. Further, how AI potentially makes marketing entities ineffective and inefficient is rarely addressed in research. Therefore, in this research, we begin with the articulation of a theory toolkit relevant to AI for marketing. Second, we discuss different types of AI and introduce a new perspective on approaching AI for marketing entities and purposes. Specifically, we conceptualize three new types of AI: enabling AI, engaging AI, and ersatzing AI (artificial, but inferior intelligence that make marketing entities less capable). Third, using our typology, we explicate the enormous potential of the three specific types of AI for marketing. Finally, toward actualizing the potential of AI for marketing, we conclude with a discussion of the contributions of our research and a research agenda.

Abstract

The rapid digitization of media, driven by technological advancements, has accelerated the spread of information through online platforms. This has heightened the need for robust fact-checking mechanisms to counter misinformation. The prevalence of misinformation necessitates the development of automated claim detection systems to support efficient automated or semi-automated fact-checking processes. Existing claim detection systems predominantly focus on the English language, with limited resources available for other regional languages like Bangla. This paper proposes a novel ensemble machine learning framework for the effective detection of claims in a low-resource language like Bangla, a critical initial step in the automated fact-checking process. The proposed weighted ensemble technique combines Support Vector Machines, Bernoulli Naive Bayes, and Decision Trees as base classifiers to effectively detect claims. An annotated text dataset comprising 5010 sentences sourced from various online platforms, including several online fact-checking sites, was developed. To determine the optimal model and feature representation for claim detection, various machine learning algorithms were evaluated using BoW, TF-IDF, Word2Vec, and FastText features. The efficacy of ensemble models was examined by investigating both averaging and weighting strategies. Evaluation metrics showcased that the proposed weighted ensemble approach outperformed all baseline models, achieving a maximum F1 score of 0.87. To the best of our knowledge, this study is the first and only approach to claim detection in the Bangla language, with the potential for extension to other resource-constrained languages. Our work aspires to serve as a crucial tool in the fight against misinformation by advancing the accuracy and transparency of information.

Abstract

This paper reviews evidence about the impact of marketing on ill health. We summarize evidence that marketing practices in six industries (tobacco, alcohol, pharmaceutical, processed food, firearm, and fossil fuel) are causal influences on the occurrence of injury, disease, and premature death. For each industry, we provide a brief overview on the extent of harmful marketing, efforts from each industry to obscure or otherwise conceal the impact of their marketing strategies, and efforts to counter the impact of harmful marketing in these industries. However, considering the ubiquitous belief that regulation is harmful to society, little headway has been made in reducing harmful marketing. We propose the substitution of a public health framework for the currently dominant free market ideology. Doing so would situate harmful marketing as a social determinant of health and consolidate the disparate efforts to regulate marketing of harmful products. Implications for future policy and research efforts are discussed.

Abstract

When COVID-19 vaccines were publicly distributed, there was inaccurate information about their efficacy and safety. Why is it that some people came to believe claims that were not corroborated by evidence and held such misbeliefs? Based on the belief gap hypothesis, we expect partisan-based gaps in misbeliefs about COVID-19 vaccines to grow over time as a function of partisan news exposure. Data from a three-wave survey fielded shortly after the COVID-19 vaccine public rollout showed no change in misbeliefs by political identity. However, data showed three-way interaction effects, such that levels of misbeliefs decreased during the study period for respondents with right-leaning political identities as they were exposed to vaccine-related news in both conservative and liberal news outlets. We discuss theoretical and practical implications of findings.

Abstract

As social media increasingly permeates everyday life, ethical concerns about its use are coming into sharper focus. At the same time, the ethical issues involved in social media marketing have received somewhat limited attention from marketing research and practice. Therefore, many scholars are calling out for a better understanding of the role of ethics in social media marketing decisions. Current marketing ethics theories do not sufficiently consider the unique dynamics of social media. To aid researchers, practitioners, and policymakers, we propose a theoretical framework to address issues in social media marketing ethics built on theories of normative and descriptive ethics. This framework considers the normative and descriptive ethics within firm-internal and firm-external factors in the fields of law, regulation and norms, platform conditions, and stakeholder value. Then, applying a thematically built systematic literature review, we identify and discuss five distinct themes of research in social media marketing ethics: (I) advertising and customer–brand relationships; (II) the dark side of social media; (III) privacy; (IV) fake news; and (V) emerging research. These themes and their implications are discussed using the proposed theoretical framework. Our study provides a comprehensive overview and synthesis of ethical challenges in social media marketing and suggests possible research avenues for the future. In doing so, it outlines pressing issues that require attention from researchers, policymakers, and practitioners to ensure an ethically sustainable approach to social media marketing.

Abstract

Many social media platforms enable (nearly) anyone to post (nearly) anything. One clear downside of this permissiveness is that many people appear bad at determining who to trust online. Hacks, quacks, climate change deniers, vaccine skeptics, and election deniers have all gained massive followings in these free markets of ideas, and many of their followers seem to genuinely trust them. At the same time, there are many cases in which people seem to reliably determine who to trust online. Consider, for example, Do It Yourself (DIY) content about how to play guitar, bake, fix one’s plumbing, or repair one’s car. For these topics, those who have the largest accounts and the most popular content typically possess significant expertise. That is, social media users seem to reliably pick out DIY experts. We thus have a puzzle: why are social media users seemingly competent at identifying DIY experts, but not climate science or vaccine experts? In what follows, we solve this puzzle. We begin by identifying a novel wisdom of the crowds phenomenon: specifically we argue that the crowd (in combination with social media search and recommendation algorithms) reliably picks out DIY experts and serves as a credentialing institution for DIY content. Next, we argue that (a) there are five epistemic factors that determine whether the crowd can succeed at recognizing experts on social media platforms, and (b) while many of those factors are satisfied to a sufficiently high degree by DIY content, they are mostly lacking for content about climate change or vaccines.

Abstract

Background

The increasing extent and severity of wildfires in the western USA poses a significant challenge to managers and to society. Forest thinning and prescribed fire treatments reduce fire hazard and improve resilience to climatic stressors. However, expanding the pace and scale of forest management is hampered, in part, by limited understanding and exposure of interested parties and the public to fuel reduction treatments. Virtual tour applications provide an opportunity to extend tours of treatment demonstration areas to anyone with a computer and internet connection. Yet there is little research on the effectiveness of virtual tours for enhancing understanding of forest treatments and if managers would deploy virtual tours to increase public awareness. Here we describe the development and evaluation of a virtual tour (https://chorophronesis.geog.psu.edu/virtualexperiences/StanislausWebsite/indexSummer2022.html) using surveys for three occupational groups: forest managers, university students, and non-student non-managers.

Results

The virtual tour improved self-reported understanding of how fires historically shaped forests, how fuels changed in the absence of fire, how thinning affects wildfire hazard, how prescribed fire affects wildfire hazard, and how thinning can be modified to enhance biodiversity. The virtual tour was also effective at conveying differences between treatment and non-treatment and among thinning and prescribed fire treatments, for all three occupational groups. There was strong agreement by all groups that if a field tour of forest treatments was not an option, the virtual tour would be a good substitute. The manager and non-manager occupation groups expressed significantly greater agreement with questions on the utility of virtual technology for aiding land management planning discussions and stimulating dialog among their own networks compared to students.

Conclusions

There was an overwhelmingly positive response to the virtual tour by all groups indicating significant potential to use virtual tours to improve understanding of fuel treatments. This could reduce social barriers impeding the scaling up of fuel reduction treatments that are needed to reduce fire hazard in California and elsewhere.