scholarly journals Susceptibility to misinformation: a study of climate change, Covid-19, and artificial intelligence

2021 ◽  
Author(s):  
Sven Gruener
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Belief System ◽  
Survey Experiment ◽  
Prior Beliefs ◽  
Conspiracy Theories ◽  
Subjective Knowledge ◽  
News Stories ◽  
False News ◽  
New Evidence

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.

scholarly journals Misinformation: determinants of gullibility

2021 ◽  
Author(s):  
Sven Gruener
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Social Networks ◽  
Risk Perception ◽  
Belief System ◽  
Survey Experiment ◽  
Level Of Education ◽  
News Stories ◽  
False News

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.

scholarly journals Sex Trafficking, Russian Infiltration, Birth Certificates, and Pedophilia: A Survey Experiment Correcting Fake News

2018 ◽  
Vol 5 (2) ◽  
pp. 159-164 ◽  
Author(s):  
Ethan Porter ◽  
Thomas J. Wood ◽  
David Kirby
Keyword(s):  
Sex Trafficking ◽  
Birth Certificates ◽  
Survey Experiment ◽  
Fake News ◽  
News Stories ◽  
False News

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?

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

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.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

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.

scholarly journals Using Artificial Intelligence to Visualize the Impacts of Climate Change

2021 ◽  
Vol 41 (1) ◽  
pp. 8-14
Author(s):  
Alexandra Luccioni ◽  
Victor Schmidt ◽  
Vahe Vardanyan ◽  
Yoshua Bengio ◽  
Theresa-Marie Rhyne
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Impacts Of Climate Change

Reducing misperceptions through news stories with data visualization: The role of readers’ prior knowledge and prior beliefs

Journalism ◽  
2021 ◽  
pp. 146488492110287
Author(s):  
Paul Mena
Keyword(s):  
Experimental Design ◽  
Prior Knowledge ◽  
Data Visualization ◽  
Accurate Information ◽  
Graphical Representations ◽  
Prior Beliefs ◽  
News Stories ◽  
Issue Involvement

Amid the global discussion on ways to fight misinformation, journalists have been writing stories with graphical representations of data to expose misperceptions and provide readers with more accurate information. Employing an experimental design, this study explored to what extent news stories correcting misperceptions are effective in reducing them when the stories include data visualization and how influential readers’ prior beliefs, issue involvement and prior knowledge may be in that context. The study found that the presence of data visualization in news articles correcting misperceptions significantly enhanced the reduction of misperceptions among news readers with less than average prior knowledge about an issue. In addition, it was found that prior beliefs had a significant effect on news readers’ misperceptions regardless of the presence or absence of data visualization. In this way, this research offers some support for the notion that data visualization may be useful to decrease misperceptions under certain circumstances.

scholarly journals The proposer or the proposal? An experimental analysis of constitutional beliefs

2021 ◽  
pp. 1-25
Author(s):  
Kenneth Mori McElwain ◽  
Shusei Eshima ◽  
Christian G. Winkler
Keyword(s):  
Experimental Analysis ◽  
The State ◽  
Survey Experiment ◽  
Prior Beliefs ◽  
Constitutional Amendments ◽  
Citizen Preferences ◽  
Government Authority ◽  
Relative Salience ◽  
Constitutional Issue

Abstract In many countries, constitutional amendments require the direct approval of voters, but the consequences of fundamental changes to the powers and operations of the state are difficult to anticipate. The referendums literature suggests that citizens weigh their prior beliefs about the merits of proposals against the heuristic provided by the partisanship of the proposer, but the relative salience of these factors across constitutional issue areas remains underexplored. This paper examines the determinants of citizen preferences on 12 diverse constitutional issues, based on a novel survey experiment in Japan. We show that support for amendments is greater when its proposer is described as non-partisan. However, constitutional ideology moderates this effect. Those who prefer idealistic constitutions that elevate national traditions tend to value proposals that expand government powers, compared to those who prefer pragmatic constitutions that constrain government authority. These results highlight the significance of constitutional beliefs that are independent of partisanship.

New Evidence of the Effect of Literacies in Reducing Disinformation and Fake News

2021 ◽  
Vol 23 (6) ◽  
pp. 300-333
Author(s):  
José Ricardo Ledur ◽  
Renato P. dos Santos
Keyword(s):  
Student Perceptions ◽  
Information Age ◽  
Literacy Skills ◽  
Fake News ◽  
9Th Grade ◽  
Data Collection And Analysis ◽  
Information Literacy Skills ◽  
The Media ◽  
False News ◽  
New Evidence

Context: The production of scientific knowledge is not clearly understood by most individuals. In the information age, society faces challenges generated by discrediting institutions, including science, the proliferation of false news, disinformation and the relativisation of truth. These are significant issues that the school cannot refrain from discussing if it wants to educate for citizenship. Objectives: To investigate how conceptions about science influence and are influenced by fake news conveyed by the media and the contribution of literacy to minimise the effects of misinformation. Design: The methodology used in this research used a mixed-methods approach through content analysis of students’ responses combined with descriptive statistical techniques. Environment and participants: The research was carried out with 32 students, divided into two groups, attending the 9th grade of an elementary public school in Bom Princípio/RS. Data collection and analysis: Two questionnaires were applied: one for the conceptions about science and another to identify fake news. Results: Most students have a limited view of science and find it difficult to identify fake news through verification criteria. A correlation between student perceptions and the identification of false news was observed. Conclusions: Knowledge about science possibly enhances students’ perception of doubtful information. It is crucial to develop mediatic and information literacy skills as they can positively impact the identification of fake news and reduce its shares.

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