GMO discussion on Twitter.

This paper focuses on analyzing discussions related to Genetically Modified Organisms (GMOs) on Twitter, with a specific focus on the spread of misinformation and conspiracy theories. The authors collected and analyzed 1,048,274 English tweets related to GMOs between January 2020 and December 2022 using the Twitter API. The tweets were subjected to topical and sentiment analysis to identify the prevalent themes and attitudes toward GMOs. 30.92% of the tweets in the observed period were negative, 21.65% were neutral, and 47.43% were positive. The authors identified four clusters of tweets associated with misinformation or conspiracy theories: GMOs and vaccines, GMOs and COVID-19, GMOs and Monsanto, and GMOs and Bill Gates. The findings of this analysis can inform strategies for combating the spread of false information and conspiracies on social media and improve public understanding and trust in GMO technology.

Dynamics and characteristics of misinformation related to earthquake predictions on Twitter.

The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020-March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more not-misinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions.

Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic.

Online learning systems have become an applied solution for delivering educational content, especially in developing countries, since the start of the COVID-19 pandemic. The present study is designed to identify the factors influencing the behavioral intention of agricultural students at universities in Iran to use online learning systems in the future. This research uses an extended model in which the constructs of Internet self-efficacy, Internet anxiety, and output quality are integrated into the technology acceptance model (TAM). Data analysis was performed using the SmartPLS technique. The analyses showed the proposed model to be strong in terms of predicting the attitude to online learning and the intention to use it. The extended TAM model fit the data well and predicted 74% of the intention variance. Our findings show attitude and perceived usefulness to have directly affected intention. Output quality and Internet self-efficacy indirectly affected attitude and intention. Research findings can help with the design of educational policies and programs to facilitate education and improve student academic performance.

A risk-based decision framework for policy analysis of societal pandemic effects.

Introduction: In this article, we summarize our findings from an EU-supported project for policy analyses applied to pandemics such as Covid-19 (with the potential to be applied as well to other, similar hazards) while considering various mitigation levels and consequence sets under several criteria. Methods: It is based on our former development for handling imprecise information in risk trees and multi-criteria hierarchies using intervals and qualitative estimates. We shortly present the theoretical background and demonstrate how it can be used for systematic policy analyses. In our model, we use decision trees and multi-criteria hierarchies extended by belief distributions for weights, probabilities and values as well as combination rules to aggregate the background information in an extended expected value model, taking into criteria weights as well as probabilities and outcome values. We used the computer-supported tool DecideIT for the aggregate decision analysis under uncertainty. Results: The framework has been applied in three countries: Botswana, Romania and Jordan, and extended for scenario-building during the third wave of the pandemic in Sweden, proving its feasibility in real-time policy-making for pandemic mitigation measures. Discussion: This work resulted in a more fine-grained model for policy decision that is much more aligned to the societal needs in the future, either if the Covid-19 pandemic prevails or for the next pandemic or other society-wide hazardous emergencies.

COVID-19 Conspiracy Theories Discussion on Twitter.

The coronavirus disease 2019 (COVID-19) pandemic was an unexpected event and resulted in catastrophic consequences with long-lasting behavioral effects. People began to seek explanations for different aspects of COVID-19 and resorted to conspiracy narratives. The objective of this article is to analyze the changes on the discussion of different COVID-19 conspiracy theories throughout the pandemic on Twitter. We have collected a data set of 1.269 million tweets associated with the discussion on conspiracy theories between January 2020 and November 2021. The data set includes tweets related to eight conspiracy theories: the 5G, Big Pharma, Bill Gates, biological weapon, exaggeration, FilmYourHospital, genetically modified organism (GMO), and the vaccines conspiracy. The analysis highlights several behaviors in the discussion of conspiracy theories and allows categorizing them into four groups. The first group are conspiracy theories that peaked at the beginning of the pandemic and sharply declined afterwards, including the 5G and FilmYourHospital conspiracies. The second group associated with the Big Pharma and vaccination-related conspiracy whose role increased as the pandemic progressed. The third are conspiracies that remained persistent throughout the pandemic such as exaggeration and Bill Gates conspiracies. The fourth are those that had multiple peaks at different times of the pandemic including the GMO and biological weapon conspiracies. In addition, the number of COVID-19 new cases was found to be a significant predictor for the next week tweet frequency for most of the conspiracies.

Analyzing Russian Media Policy on Promoting Vaccination and Other COVID-19 Risk Mitigation Measures.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in many tangible and intangible losses. To manage the risk of the pandemic and to mitigate its further spread, governments of many countries applied various pandemic risk mitigation measures. Media campaigns played a particularly large role during the pandemic, too. In addition, social media grew in importance because of the spread of technologies and as a result of the increased attention to information about COVID-19. Media information strongly influenced both the public perception of COVID-19 risk and decision-making processes and choices, which people made regarding risk reduction measures during the pandemic. Moreover, media information has had a major impact on the effectiveness and efficiency of various countries' risk management actions. Therefore, the purpose of this article is to investigate the influence of the Russian media on the population's perception of risk, and to address the question about which linguistic and psychological methods they used to shape different media discourses about the COVID-19 pandemic. Thus, we analyzed media discourses as a part of the case study of COVID-19 risk management in the Russian Federation. The theoretical basis of the study includes mass communication theories. The methodological basis consists of linguo-cognitive analysis of empirical materials for specific political-philosophical, linguistic-publicistic, and sociopsychological functioning.

Viewing the global health system as a complex adaptive system - implications for research and practice.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield.   We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

Factors affecting smallholder farmers' technical and non-technical adaptation responses to drought in Iran.

Farmers adopt a range of technical and non-technical adaptation behaviors (TANAB) to alleviate the negative effects of drought. Understanding this adaptation behavior and its determinants is essential for improving adaptation capacity and promoting sustainable agriculture. In this study, we investigated the factors affecting TANAB using the Health Belief Model (HBM) as a theoretical framework. A survey was conducted of 320 farmers in Dashtestan County, Bushehr Province, southern Iran, on the shores of Persian Gulf. We used multi-stage stratified random sampling to select the research samples and applied structural equation modeling to analyze the determinants of TANAB. The findings revealed that the self-efficacy variable is the most important predictor of TANAB. As well as self-efficacy, other determinants of technical adaptation behaviors (TAB) are perceived barriers, severity, and susceptibility. Self-efficacy and cue to action were found to determine non-technical adaptive behaviors (NAB). This study indicates the differences between the determinants of TAB and NAB and emphasizes the key role of self-efficacy. The results of this study have implications for policymakers in the agricultural sector and can help in the development of interventionist policies to improve rural development in response to environmental crises. Governments and policymakers need to reinforce capacity-building potential for agricultural extension systems and adaptation training through information and communication technologies. This investment in education is critical for sustainable agricultural development and encourages farmers to adopt appropriate drought strategies aiming reducing farmers' vulnerability.

A Multi-Criteria Framework for Pandemic Response Measures.

In managing the COVID-19 pandemic, several compelling narratives seem to have played a significant role in the decision-making processes regarding which risk mitigation and management measures to implement. Many countries were to a large extent unprepared for such a situation, even though predictions about a significant probability for a pandemic to occur existed, and national governments of several countries often acted in an uncoordinated manner, which resulted in many inconsistencies in the disaster risk reduction processes. Limited evidence has also made room for strategic narratives meant to persuade the public of the chosen set of actions, even though the degree of uncertainty regarding the outcomes of these was high, further complicating the situation. In this article, we assume a normative standpoint regarding rhapsodic decision making and suggest an integrated framework for a more elaborated decision analysis under the ambiguity of how to contain the virus spread from a policy point of view, while considering epidemiologic estimations and socioeconomic factors in a multi-stakeholder-multi-criteria context based on a co-creative work process for eliciting attitudes, perceptions, as well as preferences amongst relevant stakeholder groups. The framework, applied in our paper on Romania for demonstrative purposes, is used for evaluating mitigation measures for catastrophic events such as the COVID-19 situation, to mobilize better response strategies for future scenarios related to pandemics and other hazardous events, as well as to structure the production and analysis of narratives on the current pandemic effects.

The Use of Big Data via 5G to Alleviate Symptoms of Acute Stress Disorder Caused by Quarantine Measures.

This article investigates the role of Big Data in situations of psychological stress such as during the recent pandemic caused by the COVID-19 health crisis. Quarantine measures, which are necessary to mitigate pandemic risk, are causing severe stress symptoms to the human body including mental health. We highlight the most common impact factors and the uncertainty connected with COVID-19, quarantine measures, and the role of Big Data, namely, how Big Data can help alleviate or mitigate these effects by comparing the status quo of current technology capabilities with the potential effects of an increase of digitalization on mental health. We find that, while Big Data helps in the pre-assessment of potentially endangered persons, it also proves to be an efficient tool in alleviating the negative psychological effects of quarantine. We find evidence of the positive effects of Big Data on human health conditions by assessing the effect of internet use on mental health in 173 countries. We found positive effects in 110 countries with 90 significant results. However, increased use of digital media and exclusive exposure to digital connectivity causes negative long-term effects such as a decline in social empathy, which creates a form of psychological isolation, causing symptoms of acute stress disorder.

Cognitive theory of stress and farmers' responses to the COVID 19 shock; a model to assess coping behaviors with stress among farmers in southern Iran.

Better understanding the capacity of farmers to react to the COVID-19 pandemic shock requires analyzing their coping mechanisms. This study investigates the relationship between coping style and coping behaviors with COVID-19, identifying the psychological factors affecting them based on the cognitive theory of stress. Perceived government support is added to the original model as a predictor for coping style and behavior. Cross sectional survey data was collected among farmers living in Dashtestan county of Bushehr province in southern Iran (N = 377). To determine the strength of the model, structural equation modeling (SEM) was performed. We find that demand appraisal and perceived collective efficacy have a positive, direct, and significant effect on coping style and an indirect effect on behaviors. Coping style and perceived government support are significantly related to behavior. The research model predicts 63% and 31% of variance changes in coping style and behavior, respectively. The results of this study provide empirical evidence on how people cope with the COVID-19 shock as well as useful information for designing and implementing health and social programs and policies in the agricultural communities.

Some at Risk for COVID-19 Are Reluctant to Take Precautions, but Others Are Not: A Case From Rural in Southern Iran.

Little is known about the evaluative and cognitive foundations for adopting preventive measures to reduce the spread of COVID-19. Recognizing the existence of a gap in the knowledge describing the intention and behavior of participating in health measures, this study investigated the drivers that contribute to the intention to take health protective measures among 305 rural youth from the Dashtestan Region, Bushehr Province, and southern Iran, reached through an online survey. Protection motivation theory (PMT) served as the theoretical framework for the study. It was able to forecast variation in intentions and behaviors with accuracies of 39 and 64%, respectively. Furthermore, the variables of response efficiency, perceived severity, and self-efficacy had a positive and significant effect on protective intentions. Additionally, perceived severity, self-efficacy, and intention produced a positive and significant impression on behaviors, with most of the behavioral variance being accounted for by intention, as was hypothesized. In conclusion, it is suggested that health development including training measures that take account of both the concrete issues of health resources and technologies and of more abstract ones, such as mindset readiness, are important for engagement in positive health care behaviors. Accordingly, training-based interventions for rural youth should be contemplated, with the object of changing their intentions.

Participatory environmental governance of infrastructure projects affecting reindeer husbandry in the Arctic.

Several infrastructure projects are under development or already operational across the Arctic region. Often the deployment of such projects creates benefits at the national, regional, or global scales. However, local communities can experience negative impacts due to the requirements for extensive land areas, which cause pressure on traditional land use. Public participation in environmental planning such as Environmental Impact Assessment (EIA) enables local communities to provide feedback on the environmental, social, and economic challenges of infrastructure projects. Ideally, participation can improve the means of social learning for all involved parties and help to co-develop sustainable solutions. The subject of our research is reindeer herders' participation in EIA procedures of mines and wind farms in Finland because these types of projects affect reindeer husbandry. We study empirically how stakeholders involved in the EIAs perceive the participation of reindeer herders in the planning and implementation of infrastructure projects, and how these differ from the perceptions of the reindeer herders who are affected by the infrastructure projects. Our qualitative data is based on in-depth semi-structured interviews (N = 31) with members of the industry sector, consultants, governmental authorities, and representatives of local communities; in this study, the reindeer herders. The results show that herders' level of participation in the EIAs and the benefits and challenges of participation are perceived differently. Furthermore, the regulatory framework does not adequately ensure that the developer carries social and environmental responsibilities throughout the infrastructure project's lifetime, and that regular communication with herders will also be maintained after the EIAs. Herders' expertise should be used throughout the project lifetime. For example, more attention should be paid to both negotiating possible options for compensation and monitoring mechanisms when the infrastructure projects are pre-screened for the EIAs, as well as to co-designing the different project alternatives with herders for the EIAs.

In Search of Perfect Foresight? Policy Bias, Management of Unknowns, and What Has Changed in Science Policy Since the Tohoku Disaster.

The failure to foresee the catastrophic earthquakes, tsunamis, and nuclear accident of 2011 has been perceived by many in Japan as a fundamental shortcoming of modern disaster risk science. Hampered by a variety of cognitive and institutional biases, the conventional disaster risk management planning based on the "known risks" led to the cascading failures of the interlinked disaster risk management (DRM) apparatus. This realization led to a major rethinking in the use of science for policy and the incorporations of lessons learned in the country's new DRM policy. This study reviews publicly available documents on expert committee discussions and scientific articles to identify what continuities and changes have been made in the use of scientific knowledge in Japanese risk management. In general, the prior influence of cognitive bias (e.g., overreliance on documented hazard risks) has been largely recognized, and increased attention is now being paid to the incorporation of less documented but known risks. This has led to upward adjustments in estimated damages from future risks and recognition of the need for further strengthening of DRM policy. At the same time, there remains significant continuity in the way scientific knowledge is perceived to provide sufficient and justifiable grounds for the development and implementation of DRM policy. The emphasis on "evidence-based policy" in earthquake and tsunami risk reduction measures continues, despite the critical reflections of a group of scientists who advocate for a major rethinking of the country's science-policy institution respecting the limitations of the current state science.