Tag: Climate Change Disinformation

Abstract

Calls for public engagement on emerging agricultural technologies, including genetic engineering of farm animals, have resulted in the development of information that people can interact and engage with online, including “Frequently Asked Questions” (FAQs) developed by organizations seeking to inform or influence the debate. We conducted a frame analysis of FAQs webpages about genetic engineering of farm animals developed by different organizations to describe how questions and answers are presented. We categorized FAQs as having a regulatory frame (emphasizing or challenging the adequacy of regulations), an efficiency frame (emphasizing precision and benefits), a risks and uncertainty frame (emphasizing unknown outcomes), an animal welfare frame (emphasizing benefits for animals) or an animal dignity frame (considering the inherent value of animals). Animals were often featured as the object of regulations in FAQs, and questions about animals were linked to animal welfare regulations. The public were represented using a variety of terms (public, consumer) and pronouns (I, we). Some FAQs described differences between technology terms (gene editing, genetic modification) and categorized technologies as either well-established or novel. This framing of the technology may not respond to actual public concerns on the topic. Organizations seeking to use FAQs as a public engagement tool might consider including multiple viewpoints and actual questions people have about genetic engineering.

Abstract

Marine ecosystem-based management (EBM) is recognized as the best practice for managing multiple ocean-use sectors, explicitly addressing tradeoffs among them. However, implementation is perceived as challenging and often slow. A poll of over 150 international EBM experts revealed progress, challenges, and solutions in EBM implementation worldwide. Subsequent follow-up discussions with over 40 of these experts identified remaining impediments to further implementation of EBM: governance; stakeholder engagement; support; uncertainty about and understanding of EBM; technology and data; communication and marketing. EBM is often portrayed as too complex or too challenging to be fully implemented, but we report that identifiable and achievable solutions exist (e.g., political will, persistence, capacity building, changing incentives, and strategic marketing of EBM), for most of these challenges and some solutions can solve many impediments simultaneously. Furthermore, we are advancing in key components of EBM by practitioners who may not necessarily realize they are doing so under different paradigms. These findings indicate substantial progress on EBM, more than previously reported.

Abstract

Assessments of future droughts are essential tools due to the potential for serious damage to the environment, economy, and society, particularly under climate change. This study proposes a framework for assessing drought characteristics at different scales, periods, and emission scenarios modeled by phase 6 of the Coupled Model Intercomparison Project. The four drought characteristics were determined by applying the Run theory to a standardized precipitation index time series, and the severe drought areas were detected by the Jenks Natural Breaks and Kriging methods. The study produced four main findings. (1) A stochastic weather generator, AWE-GEN, captures the variability of precipitation with inter- and intra-annual stochastic properties, and presents naturally occurring variability as an ensemble. (2) According to the ensemble average of drought characteristics, future droughts are projected to become less frequent with similar durations and intensity due to future rise in precipitation. However, the ensemble (stochastic or natural) and spatial variabilities are expected to increase, making drought management difficult (e.g., future decrease of 18% in \({DE}_{max}\) for END585). (3) Different temporal scales can affect the detection and characterization of drought events. Smaller temporal scales identify mild drought events of short duration and low severity, while larger scales merge and extend drought events, resulting in more prolonged and severe droughts. (4) Severe drought areas can expand compared with a control period for drought duration and severity, but may decrease for drought interval and frequency especially for the END period (e.g., 24% and 17% increase for \({DD}_{max}\) and \({\left|DS\right|}_{max}\) , and 85% and 78% decrease for \({DI}_{mean}\) and \({DE}_{max}\) for SPI3 and END585). The framework proposed in this study is expected to provide important information for the building of strategies required to adapt to and mitigate the potential impacts of drought in the future.

Abstract

This paper argues that section 230 of the Communications Decency Act should not protect tech companies for their role in behavioral advertising and designing and using algorithms that ensure the spread of dangerous content, including ISIS and far right-wing recruiting videos, propaganda, and other harmful misinformation. Under the broad reading of 230, I argue that tech companies are serving two roles and getting immunity for both: they provide the blank medium, and they propel ideologically bundled snippets of information to those most vulnerable to absorbing the radical viewpoints in them. These two roles are distinct, and the second role has little to do with free speech or creating a level playing field for public discourse. Search engines, and social media like TikTok, Facebook, Instagram, Snap, Reddit, and YouTube are not blank slates for posting content, they are ecosystems with complex designs. Their business plans and algorithms lead to a “rabbit-hole effect” that poses danger. There are public policy approaches that could reduce harm and offer solutions compatible with free speech and a narrower 230 immunity.

Abstract

The relevance of the research is defined by the need to create a set of linguistic means, which would contribute to effective communication with the general public, and the need to study different functional-semantic categories, including appeals, for the competent formation of public opinion in the political context. The research aims to comprehend the functioning of linguistic means used as appeals in the example of political propaganda texts in the English media field. The methodology is based on the theoretical study of the works of modern linguists, linguistic, structural, and communicative analysis of appeal linguistic units and contexts. The research considered the linguistic means that form the functional-semantic category of the appeal, examples of political contexts from the British and American media were presented, the functions of appeal were presented using specific examples, an idea of the communicative side of political propaganda texts and audience participation in this process was formed, emphasis was placed on different types of propaganda (white, black, gray), the following groups of appeals were characterized: imperatives (volitional and involuntary). The materials presented in this research can be used to form an idea of the functional-semantic category of appeal, the choice of linguistic means for the purpose of information promotion in the media or social networks, the study of communicative strategies in linguistics and their successful implementation, consideration of political propaganda texts, increasing efficiency when influencing the audience, further implementation of means of appeal in machine learning.

Abstract

The relevance of the research is defined by the need to create a set of linguistic means, which would contribute to effective communication with the general public, and the need to study different functional-semantic categories, including appeals, for the competent formation of public opinion in the political context. The research aims to comprehend the functioning of linguistic means used as appeals in the example of political propaganda texts in the English media field. The methodology is based on the theoretical study of the works of modern linguists, linguistic, structural, and communicative analysis of appeal linguistic units and contexts. The research considered the linguistic means that form the functional-semantic category of the appeal, examples of political contexts from the British and American media were presented, the functions of appeal were presented using specific examples, an idea of the communicative side of political propaganda texts and audience participation in this process was formed, emphasis was placed on different types of propaganda (white, black, gray), the following groups of appeals were characterized: imperatives (volitional and involuntary). The materials presented in this research can be used to form an idea of the functional-semantic category of appeal, the choice of linguistic means for the purpose of information promotion in the media or social networks, the study of communicative strategies in linguistics and their successful implementation, consideration of political propaganda texts, increasing efficiency when influencing the audience, further implementation of means of appeal in machine learning.

Abstract

In the digital age, poor public communication catalyzes the spread of disinformation within public opinion. Anyone can produce political content that can reach a global audience, and social media has become a vital tool for political leaders to convey messages to the electorate. The 2022 Italian election campaign has seen the term “nuclear” debated with two different declinations: on the one hand, regarding nuclear energy for civilian use, and on the other hand, regarding the fear of an escalation of the conflict in Ukraine and the use of atomic weapons. This research aims to analyze the social media debate by exploring multiplatform dynamics to qualitatively identify and analyze the connections between social media platforms that we have termed Bridges, a concept drawn from Transmedia Theory to describe the narrative relationship between platforms. The methodological approach will follow an explanatory sequential design that will rely on digital methods to identify connections between platforms (bridges) and then apply an exploratory qualitative approach to enrich the data and capture the nuances of the debate. As expected, we found polarized positions and fragmentation on both issues of civilian nuclear energy and the atomic conflict narrative. Primary evidence shows bridges spreading affective, defective, and infective content across platforms in a multifaceted social media ecosystem. Affective refers to rhetoric that appeals to people’s feelings. Defective means the discussion that brings attention to hyper-partisan news channels, fake news, and misinformation. Infective means bridges with below-the-radar platforms, niche channels, or pseudo-information channels. They use bridges with mainstream platforms to gain the potential to go viral. The paper highlights the importance of cross-platform and interdisciplinary approaches to addressing disinformation in a media ecosystem where social media plays an increasing role in a country’s democratic dynamics.

Abstract

In the digital age, poor public communication catalyzes the spread of disinformation within public opinion. Anyone can produce political content that can reach a global audience, and social media has become a vital tool for political leaders to convey messages to the electorate. The 2022 Italian election campaign has seen the term “nuclear” debated with two different declinations: on the one hand, regarding nuclear energy for civilian use, and on the other hand, regarding the fear of an escalation of the conflict in Ukraine and the use of atomic weapons. This research aims to analyze the social media debate by exploring multiplatform dynamics to qualitatively identify and analyze the connections between social media platforms that we have termed Bridges, a concept drawn from Transmedia Theory to describe the narrative relationship between platforms. The methodological approach will follow an explanatory sequential design that will rely on digital methods to identify connections between platforms (bridges) and then apply an exploratory qualitative approach to enrich the data and capture the nuances of the debate. As expected, we found polarized positions and fragmentation on both issues of civilian nuclear energy and the atomic conflict narrative. Primary evidence shows bridges spreading affective, defective, and infective content across platforms in a multifaceted social media ecosystem. Affective refers to rhetoric that appeals to people’s feelings. Defective means the discussion that brings attention to hyper-partisan news channels, fake news, and misinformation. Infective means bridges with below-the-radar platforms, niche channels, or pseudo-information channels. They use bridges with mainstream platforms to gain the potential to go viral. The paper highlights the importance of cross-platform and interdisciplinary approaches to addressing disinformation in a media ecosystem where social media plays an increasing role in a country’s democratic dynamics.

Abstract

In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint, we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% prediction accuracy for the test set. Testing our prediction on two additional models results in 90.7% and 90.5% accuracy, validating our model. The predictive and validation results suggest that building a machine learning model around social functions within the Russian influence network can be used to map its actors and functions.

Abstract

Large language models (LLMs) like GPT-4 have the potential to dramatically change the landscape of influence operations. They can generate persuasive, tailored content at scale, making campaigns using falsified content, such as disinformation and fake accounts, easier to produce. Advances in self-hosted open-source models have meant that adversaries can evade content moderation and security checks built into large commercial models such as those commercialised by Anthropic, Google, and OpenAI. New multi-lingual models make it easier than ever for foreign adversaries to pose as local actors. This article examines the heightened threats posed by synthetic media, as well as the potential that these tools hold for creating effective countermeasures. It begins with assessing the challenges posed by a toxic combination of automated bots, human-controlled troll accounts, and more targeted social engineering operations. However, the second part of the article assesses the potential for these same tools to improve detection. Promising countermeasures include running internal generative models to bolster training data for internal classifiers, detecting statistical anomalies, identifying output from common prompts, and building specialised classifiers optimised for specific monitoring needs.