Tag: Climate Change Disinformation

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.

Abstract

The confrontations of stakeholder visions about agriculture and food production has become a focal point in the public sphere, coinciding with a diversification of agrifood models. This study analyzes the debates stemming from the coexistence of these models, particularly during the initial term of neoliberal-centrist Emmanuel Macron’s presidency in France. Employing collective monitoring from 2017 to 2021, a corpus of 958 online news and blog articles was compiled. Using a computational analysis, we reveal the framings and controversies emerging from this media discourse. The macro-structuring of discourse on model coexistence revolves around scientific, economic and political framings. Coexistence is a complex of debates based on specific frames associated with specific arenas and actor configurations: growth of organic agriculture, transformations of agrifood systems, sciences of production and impacts, livestock and meat diet controversies, agroecological innovations, CAP reform criticism, discourse of peasant agriculture and State-Profession co-gestion. Employing global sentiment analysis and focusing on salient controversies, namely EGAlim law, pesticide regulations, and agribashing, we show the shift from conciliation to a hardening of debates. Finally, we discuss the causes and consequences of this trend. The political will to support the transition of agriculture remains influenced by the co-gestion system, an inherited configuration of decision-makers instrumental in the agricultural modernization. As a consequence, significant agricultural challenges, particularly highlighted in the scientific macro-frame, persist unresolved. This lock-in of the agrifood system is based on defensive strategies that challenge the democratic debate about food and agricultural practices.

Abstract

Türkiye has a long history of devastating earthquakes, and on February 6, 2023, the region experienced two major earthquakes with magnitudes of 7.7 and 7.6, striking Pazarcık and Elbistan, Kahramanmaraş, respectively, on the East Anatolian Fault Zone. These earthquakes resulted in significant loss of life and property, impacting multiple cities across 11 cities, and leaving a lasting impact on the country. The 2023 Kahramanmaraş Earthquakes rank among the deadliest and most damaging earthquakes in Türkiye, alongside the historical significance of the 1939 Erzincan Earthquake and the 1999 Marmara Earthquake. Despite reforms following the 1999 Marmara Earthquake in disaster policy and preparedness, the scale of damage from the February 6 earthquakes has been shocking, necessitating further insights and lessons for future earthquake management. This paper presents the outcomes of immediate response efforts organized after the 2023 Kahramanmaraş earthquakes to elucidate emergency response activities and their impacts on communities, considering the substantial size and severity of the damages. The study focuses on evaluating the emergency response provided within the first 24 h, 3 days, and 2 weeks after the earthquakes, aiming to promptly identify the nature and effectiveness of these responses, as well as the conditions that hindered their efficacy. By shedding light on the specific experiences and challenges faced during these crucial timeframes, the research aims to offer valuable insights and lessons learned. These findings contribute to improved preparedness strategies and more efficient emergency response measures needed in responding to future disaster scenarios. Ultimately, this study provides a useful resource for all stakeholders involved in emergency response and disaster management, offering valuable guidance to enhance resilience and preparedness in the face of seismic hazards.

Abstract

Under the threat of the novel coronavirus, people are compelled to contemplate some ultimate existential questions, such as life and death. This study collected texts related to the death psychology from Sina Weibo, and after data cleaning, a total of 3868 Weibo texts were included. Study 1 employed grounded theory from qualitative research to explore the core categories and evolutionary mechanisms of people's psychology when facing death threats in the context of the pandemic. Study 2 utilized big data mining techniques such as topic mining and semantic network analysis to validate the effectiveness of the death psychology theory developed in qualitative research. The findings demonstrate that within the “Emotion–Cognition–Behavior-Value” framework, the implications of death threats manifest in four aspects: death anxiety, death cognition, coping efficacy, and sense of meaning. As time progresses, the study of death psychology can be segmented into four distinct phases: the tranquil phase prior to lifting pandemic restrictions, the threat phase at lifting pandemic restrictions onset, the coping phase mid-lifting pandemic restrictions, and the reformative phase post-lifting pandemic restrictions. The calculated outcomes of topic mining and semantic network analysis corroborate the coding results and theories derived from the grounded theory. This reaffirms that data mining technology can be a potent tool for validating grounded theory.

Abstract

Effective digital literacy interventions can positively influence social media users’ ability to identify fake news content. This research aimed to (a) introduce a new experiential training digital literacy intervention strategy, (b) evaluate the effect of different digital literacy interventions (i.e., priming critical thinking and an experiential training exercise) on the perceived accuracy of fake news and individuals’ subsequent online behavioral intentions, and (c) explore the underlying mechanisms that link various digital literacy interventions with the perceived accuracy of fake news and online behavioral intentions. The authors conducted a study, leveraging online experimental data from 609 participants. Participants were randomly assigned to different digital literacy interventions. Next, participants were shown a Tweeter tweet containing fake news story about the housing crisis and asked to evaluate the tweet in terms of its accuracy and self-report their intentions to engage in online activities related to it. They also reported their perceptions of skepticism and content diagnosticity. Both interventions were more effective than a control condition in improving participants’ ability to identify fake news messages. The findings suggest that the digital literacy interventions are associated with intentions to engage in online activities through a serial mediation model with three mediators, namely, skepticism, perceived accuracy and content diagnosticity. The results point to a need for broader application of experiential interventions on social media platforms to promote news consumers’ ability to identify fake news content.

Abstract

Effective digital literacy interventions can positively influence social media users’ ability to identify fake news content. This research aimed to (a) introduce a new experiential training digital literacy intervention strategy, (b) evaluate the effect of different digital literacy interventions (i.e., priming critical thinking and an experiential training exercise) on the perceived accuracy of fake news and individuals’ subsequent online behavioral intentions, and (c) explore the underlying mechanisms that link various digital literacy interventions with the perceived accuracy of fake news and online behavioral intentions. The authors conducted a study, leveraging online experimental data from 609 participants. Participants were randomly assigned to different digital literacy interventions. Next, participants were shown a Tweeter tweet containing fake news story about the housing crisis and asked to evaluate the tweet in terms of its accuracy and self-report their intentions to engage in online activities related to it. They also reported their perceptions of skepticism and content diagnosticity. Both interventions were more effective than a control condition in improving participants’ ability to identify fake news messages. The findings suggest that the digital literacy interventions are associated with intentions to engage in online activities through a serial mediation model with three mediators, namely, skepticism, perceived accuracy and content diagnosticity. The results point to a need for broader application of experiential interventions on social media platforms to promote news consumers’ ability to identify fake news content.