scholarly journals Misinformation: determinants of gullibility

2021 ◽  
Author(s):  
Sven Gruener

This paper analyzes the susceptibility to misinformation in a survey experiment by considering three hand-picked topics (climate change, Covid-19, and artificial intelligence). Subjects had to rate the reliability of several statements within these fields. We find evidence for a monological belief system (i.e., being susceptible to one statement containing misinformation is correlated with falling to other false news stories). Moreover, trust in social networks is positively associated with falling for misinformation. Whereas, there is some evidence that risk perception, willingness to think deliberately, actively open-minded thinking, and trust in science and media protects against being susceptible to misinformation. Surprisingly, the level of education does not seem to matter much.

2021 ◽  
Author(s):  
Sven Gruener

This study explores whether susceptibility to misinformation is context dependent. We conduct a survey experiment in which subjects had to rate the reliability of several statements in the fields of climate change, Covid-19, and artificial intelligence. There is some evidence for a monological belief system, i.e., being susceptible to one statement containing misinformation is correlated with falling to other false news stories, in all three contexts. The main findings to explain the susceptibility to misinformation can be summarized as follows: trust in social networks is positively associated with falling for misinformation in all contexts. There are also several context-related differences: Individuals are less likely to be susceptible to misinformation in the contexts of climate change and Covid-19 if they have a higher risk perception, tend to take a second look at a problem (i.e., willingness to think deliberately), update their prior beliefs to new evidence (actively open-minded thinking), and trust in science and mass media. Within the context of artificial intelligence, being less prone to conspiracy theories in general and lower subjective knowledge helps not to be susceptible to misinformation.


2020 ◽  
Vol 8 (`10) ◽  
pp. 403-410
Author(s):  
David Araújo Pinheiro ◽  
Mariana Gomes Leitão De Araújo ◽  
Keilla Barbosa De Souza ◽  
Beatriz de Sousa Campos ◽  
Evanete Maria De Oliveira ◽  
...  

In the current scenario of the COVID-19 pandemic, a lot of false information has spread through social networks. This study aimed to characterize the types of fake news in health and the factors that influence its sharing. This is a descriptive cross-sectional observational study conducted by health scholars who analyzed the messages received in the WhatsApp network and the sociodemographic characteristics of sharers in the year 2020. Results: The level of education influences the spread of false news, and family members have a higher frequency of sharing these news. As for the type of content of fake news, the fabricated content and false context stood out as the most shared ones. The characteristic of the group of researchers may have influenced the receivement of a smaller amount of fake news, since they are able to recognize and refute


2018 ◽  
Vol 5 (2) ◽  
pp. 159-164 ◽  
Author(s):  
Ethan Porter ◽  
Thomas J. Wood ◽  
David Kirby

Following the 2016 U.S. election, researchers and policymakers have become intensely concerned about the dissemination of “fake news,” or false news stories in circulation (Lazer et al., 2017). Research indicates that fake news is shared widely and has a pro-Republican tilt (Allcott and Gentzkow, 2017). Facebook now flags dubious stories as disputed and tries to block fake news publishers (Mosseri, 2016). While the typical misstatements of politicians can be corrected (Nyhan et al., 2017), the sheer depth of fake news’s conspiracizing may preclude correction. Can fake news be corrected?


Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.


2017 ◽  
Vol 45 (5) ◽  
pp. 773-784 ◽  
Author(s):  
Mikyoung Kim ◽  
Yoonhyeung Choi

We examined the main effect of message appeal (emotional and logical) and coping style (monitors and blunters) and the interaction effect between the two on risk message processing outcomes. Participants were 74 U.S. undergraduate and graduate students who read news stories about tornadoes, then rated their risk message processing outcomes. Results showed that emotional appeals led to a higher risk perception, probability of risk occurrence, and more accurate recognition memory than did logical appeals. Further, we found significant interaction effects between message appeal and coping style on risk perception. When message appeals were emotional, monitors perceived a higher risk and probability of risk occurrence than did blunters; however, when message appeals were logical, this difference between monitors and blunters disappeared. The findings suggest that (a) emotional appeals should be included in risk communication and (b) coping styles should be considered in effective risk communication.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Laura Cameron ◽  
Rhéa Rocque ◽  
Kailey Penner ◽  
Ian Mauro

Abstract Background Despite scientific evidence that climate change has profound and far reaching implications for public health, translating this knowledge in a manner that supports citizen engagement, applied decision-making, and behavioural change can be challenging. This is especially true for complex vector-borne zoonotic diseases such as Lyme disease, a tick-borne disease which is increasing in range and impact across Canada and internationally in large part due to climate change. This exploratory research aims to better understand public risk perceptions of climate change and Lyme disease in order to increase engagement and motivate behavioural change. Methods A focus group study involving 61 participants was conducted in three communities in the Canadian Prairie province of Manitoba in 2019. Focus groups were segmented by urban, rural, and urban-rural geographies, and between participants with high and low levels of self-reported concern regarding climate change. Results Findings indicate a broad range of knowledge and risk perceptions on both climate change and Lyme disease, which seem to reflect the controversy and complexity of both issues in the larger public discourse. Participants in high climate concern groups were found to have greater climate change knowledge, higher perception of risk, and less skepticism than those in low concern groups. Participants outside of the urban centre were found to have more familiarity with ticks, Lyme disease, and preventative behaviours, identifying differential sources of resilience and vulnerability. Risk perceptions of climate change and Lyme disease were found to vary independently rather than correlate, meaning that high climate change risk perception did not necessarily indicate high Lyme disease risk perception and vice versa. Conclusions This research contributes to the growing literature framing climate change as a public health issue, and suggests that in certain cases climate and health messages might be framed in a way that strategically decouples the issue when addressing climate skeptical audiences. A model showing the potential relationship between Lyme disease and climate change perceptions is proposed, and implications for engagement on climate change health impacts are discussed.


2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.


2021 ◽  
Vol 41 (1) ◽  
pp. 8-14
Author(s):  
Alexandra Luccioni ◽  
Victor Schmidt ◽  
Vahe Vardanyan ◽  
Yoshua Bengio ◽  
Theresa-Marie Rhyne

Author(s):  
Kaijing Xue ◽  
Shili Guo ◽  
Yi Liu ◽  
Shaoquan Liu ◽  
Dingde Xu

Individual perception of disaster risk is not only the product of individual factors, but also the product of social interactions. However, few studies have empirically explored the correlations between rural residents’ flat social networks, trust in pyramidal channels, and disaster-risk perceptions. Taking Sichuan Province—a typical disaster-prone province in China—as an example and using data from 327 rural households in mountainous areas threatened by multiple disasters, this paper measured the level of participants’ disaster-risk perception in the four dimensions of possibility, threat, self-efficacy, and response efficacy. Then, the ordinary least squares method was applied to probe the correlations between social networks, trust, and residents’ disaster-risk perception. The results revealed four main findings. (1) Compared with scores relating to comprehensive disaster-risk perception, participants had lower perception scores relating to possibility and threat, and higher perception scores relating to self-efficacy and response efficacy. (2) The carrier characteristics of their social networks significantly affected rural residents’ perceived levels of disaster risk, while the background characteristics did not. (3) Different dimensions of trust had distinct effects on rural residents’ disaster-risk perceptions. (4) Compared with social network variables, trust was more closely related to the perceived level of disaster risks, which was especially reflected in the impact on self-efficacy, response efficacy, and comprehensive perception. The findings of this study deepen understanding of the relationship between social networks, trust, and disaster-risk perceptions of rural residents in mountainous areas threatened by multiple disasters, providing enlightenment for building resilient disaster-prevention systems in the community.


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