Tag: Climate Change Disinformation

Abstract

Social media has revolutionized the way information is distributed throughout society, as folks continue to rely entirely on these apps for information on current events, health protocols, and socio-political discussions. However, these containers of knowledge do not appear in the same shape for every user; Algorithms, informed by capitalist agendas, determine what information sifts through its networks and to whom. Data scientists, researchers, and activists have dissected the hidden mechanics fueling these popular platforms, inciting critical conversations around the harmful biases embedded in algorithms. These studies often skim the surface of how these algorithms digitally marginalize people of color, if acknowledging it at all. Even fewer have attempted to examine the role these algorithms play in the social activism and digital community organizing happening amongst communities of color via social media. Referring to indigenous scholar Marisa Elena Duarte’s book Network Sovereignty as a framework of thought, this paper roots itself in the notion that technology is an extension of the agenda utilizing it. Through dissecting the algorithmic structures of popular social platforms among communities of color, this paper examines how social media distributes information within its networks and how its encoded biases silence Black, brown, and indigenous voices. This paper also provides insight into how BIPOC content-creators, when informed on how the algorithms work, can use these platforms to their advantage and effectively facilitate socio-political discourse online; Changing the narrative of social networks from being yet another landscape of white supremacy to instead a communal canvas for radical change.

Abstract

Public engagement on socioscientific issues is crucial to explore solutions to different crises facing humanity today. It is vital for fostering transformative change. Yet, assumptions shape whether, when and how engagement happens on a pressing issue like climate change. Here we examine three dominant assumptions—engaging the public involves power-sharing and not just information, investing in relationships can lead to mutually desirable outcomes, and more interaction is better to support engagement in climate change governance. Furthermore, we explore the implications of these assumptions and related contradictions. We offer insights to stimulate discussion on the need to understand, assess and revise implicit assumptions that might undermine the capacity to transform public engagement on climate change.

Abstract

Teaching and learning relies on age-appropriate, credible formal (e.g., textbooks, textbook supplements) and informal (e.g., trade-books) curricular texts. Previous research traced American publishers’ self-censorship about human evolution within twentieth-century textbooks. This study, informed by the latest scientific understandings, engaged in content analysis of scientific depictions of evolution, race, (homo)sexuality, and intersex identity. The data pool contained American biology textbooks, trade-books, and curricular supplements published after 1990 (n = 153). Findings revealed age-appropriate, comprehensive evolutionary depictions, yet stark omissions of scientific evidence and arguments challenging white supremacy, cisheteronormativity, and pathologization of racialized, queer, and intersex identities. Most modern biology curricular texts, in other words, disregard scientific examination of the tenets grounding racism, homophobia, and transphobia. Why do most biology curricular resources omit the science controverting prominent pseudoscientific fears? Who determines what is taught? The consequences of curricular omissions are particularly alarming considering the violence and violent threats targeting already-marginalized people.

Abstract

The COVID-19 pandemic upended daily life and disrupted human activity in urban centers all over the world. Stay-at-home orders emptied urban spaces, removing or decreasing stressors on urban wildlife associated with human presence. Anecdotal observations of unusual urban wildlife behavior spread virally across social media, but some of these reports were proven false or fabricated. Here we examined both scientific publications and local newspapers to understand how extensively urban catastrophes are covered with respect to their effects on wildlife. We read all article titles from January 1980–June 2023 in 100 high impact journals in biology to determine if prior research exists that could inform our understanding of this phenomenon. Additionally, we used a keyword search to find scientific journal articles about wildlife responses during events in which large-scale evacuations of urban environments occurred. We found 37 scientific articles on this topic, with 13 of those published in the highest impact biology journals. The majority of publications identified (70%) were about wildlife responses to the COVID-19 public health response. Finally, we searched local newspapers in areas where hurricanes struck urban centers. We found 25 newspaper articles reporting on wildlife in relation to urban natural disasters. These were typically anecdotes, but nearly always consulted a credible, expert source. Ultimately, more research focused on urban areas before and after catastrophic or sudden changes will allow biologists to develop a baseline expectation for urban wildlife behavior in the absence of humans.

Abstract

The literature on COVID-19 vaccination has rarely taken a macro and longitudinal approach to investigate the nuanced racial and ethnic differences in vaccine hesitancy and refusal. To fill this gap, this study examines the relationships between race, time, and COVID-19 vaccine hesitancy and refusal using state-level data from the US Census Household Pulse Survey, 2020 US Decennial Census, and other sources (i.e., American Community Survey, Human Development Index database, and Centers for Disease Control and Prevention). Four longitudinal Generalized Estimating Equations (GEEs) were estimated to analyze how time-variant and time-invariant measures, and time itself influenced COVID-19 vaccine hesitancy and refusal rates, controlling for the effect of other relevant covariates. The results provide descriptive evidence that COVID-19 vaccine hesitancy had decreased in the USA, but vaccine refusal remained stable between January and October 2021. The GEEs further indicated that the proportion of the Black population was positively associated with both vaccine hesitancy and refusal rates, while the proportion of the White population was positively associated with the vaccine refusal rate but not associated with the vaccine hesitancy rate. In addition, over the 10-month period, COVID-19 vaccine hesitancy and refusal in the Black population declined rapidly, but vaccine refusal in the White population stayed fairly stable. More research and practical efforts are needed to understand and inform the public about these important but overlooked trends.

Abstract

Antimicrobial resistance (AMR) — the ability of microorganisms to adapt and survive under diverse chemical selection pressures — is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted ‘One Health’ approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.

Abstract

With the expanse of technology, people are constantly exposed to an abundance of information. Of vital importance is to understand how people assess the truthfulness of such information. One indicator of perceived truthfulness seems to be whether it is repeated. That is, people tend to perceive repeated information, regardless of its veracity, as more truthful than new information, also known as the illusory truth effect. In the present study, we examined whether such effect is also observed for opinions and whether the manner in which the information is encoded influenced the illusory truth effect. Across three experiments, participants (n = 552) were presented with a list of true information, misinformation, general opinion, and/or social–political opinion statements. First, participants were either instructed to indicate whether the presented statement was a fact or opinion based on its syntax structure (Exp. 1 & 2) or assign each statement to a topic category (Exp. 3). Subsequently, participants rated the truthfulness of various new and repeated statements. Results showed that repeated information, regardless of the type of information, received higher subjective truth ratings when participants simply encoded them by assigning each statement to a topic. However, when general and social–political opinions were encoded as an opinion, we found no evidence of such effect. Moreover, we found a reversed illusory truth effect for general opinion statements when only considering information that was encoded as an opinion. These findings suggest that how information is encoded plays a crucial role in evaluating truth.

Abstract

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19.

Abstract

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19.

Abstract

Uncovering the public discourse on hydrogen energy is essential for understanding public behaviour and the evolving nature of conversations over time and across different regions. This paper presents a comprehensive analysis of a large multilingual dataset pertaining to hydrogen energy collected from Twitter spanning a decade (2013–2022) using selected keywords. The analysis aims to explore various aspects, including the temporal and spatial dimensions of the discourse, factors influencing Twitter engagement, user engagement patterns, and the interpretation of conversations through hashtags and ngrams. By delving into these aspects, this study offers valuable insights into the dynamics of public discourse surrounding hydrogen energy and the perceptions of social media users.