scholarly journals A Reasoned Approach to Dealing With Fake News

2019 ◽  
Vol 6 (1) ◽  
pp. 94-101 ◽  
Author(s):  
M. Anne Britt ◽  
Jean-François Rouet ◽  
Dylan Blaum ◽  
Keith Millis
Keyword(s):  
Decision Making ◽  
Systematic Evaluation ◽  
Affective Processing ◽  
Fake News ◽  
False Information ◽  
Information Evaluation ◽  
Personal Decision ◽  
Incorrect Information ◽  
The Web

We now have almost no filters on information that we can access, and this requires a much more vigilant, knowledgeable reader. Learning false information from the web can have dire consequences for personal, social, and personal decision making. Given how our memory works and our biases in selecting and interpreting information, now more than ever we must control our own cognitive and affective processing. As examples: Simply repeating information can increase confidence in its perceived truth; initial incorrect information remains available and can continue to have an effect despite learning the corrected information; and we are more likely to accept information that is consistent with our beliefs. Information evaluation requires readers (a) to set and monitor their goals of accuracy, coherence, and completeness; (b) to employ strategies to achieve these goals; and (c) to value this time- and effort-consuming systematic evaluation. Several recommendations support a reasoned approach to fake news and manipulation.

scholarly journals UNDERSTANDING ‘FAKE NEWS’: A BIBLIOGRAPHIC PERSPECTIVE

2020 ◽  
pp. 141-172
Author(s):  
Andrew Park ◽  
Matteo Montecchi ◽  
Cai ‘Mitsu’ Feng ◽  
Kirk Plangger ◽  
Leyland Pitt
Keyword(s):  
Web Of Science ◽  
Strategic Communication ◽  
Media Content ◽  
Fake News ◽  
False Information ◽  
Policy Makers ◽  
Bibliographic Analysis ◽  
Global Policy ◽  
Current State ◽  
The Web

False information that appears similar to trustworthy media content, or what is commonly referred to as ‘fake news’, is pervasive in both traditional and digital strategic communication channels. This paper presents a comprehensive bibliographic analysis of published academic articles related to ‘fake news’ and the related concepts of truthiness, post-factuality, and deepfakes. Using the Web of Science database and VOSViewer software, papers published on these topics were extracted and analysed to identify and visualise key trends, influential authors, and journals focusing on these topics. Articles in our dataset tend to cite authors, papers, and journals that are also within the dataset, suggesting that the conversation surrounding ‘fake news’ is still relatively centralised. Based on our findings, this paper develops a conceptual ‘fake news’ framework—derived from variations of the intention to deceive and/or harm—classifying ‘fake news’ into four subtypes: mis-information, dis-information, mal-information, and non-information. We conclude that most existing studies of ‘fake news’ investigate mis-information and dis-information, thus we suggest further study of mal-information and non-information. This paper helps scholars, practitioners, and global policy makers who wish to understand the current state of the academic conversation related to ‘fake news’, and to determine important areas for further research.

scholarly journals Faultless Decision Making for False Information in Online: A Systematic Approach

2021 ◽  
Vol 2 (4) ◽  
pp. 226-235
Author(s):  
Yasir Babiker Hamdan ◽  
Sathish
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Systematic Approach ◽  
Learning Algorithm ◽  
Research Work ◽  
High Accuracy ◽  
Fake News ◽  
False Information ◽  
Q Learning ◽  
Deep Learning Algorithm

An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.

scholarly journals EFFECTS OF FALSE NEWS ON DECISION MAKING, ACCORDING TO ISLAMIC OUTLOOK

2019 ◽  
Vol 1 (1) ◽  
pp. 39-44
Author(s):  
Shama Razi ◽  
◽  
Hamma Jillani ◽  
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Fake News ◽  
Rational Model ◽  
False Information ◽  
Moral Principles ◽  
Adverse Impacts ◽  
Administrative Model ◽  
False News ◽  
Decision Making Model

In Islam, there is strict prohibition of sneering at people, mocking and bad-mouthing. Islamic perspective shows spreading of such news which isn’t verified and is solely on the basis of guess, suspicion and delusions is prohibited. Moreover, Muslims are forbidden of spreading rumors and false information/news without any verification. There are different models studied under the decision making such as a) rational model, b) the administrative model, and c) the Retrospective Decision-Making Model. Fabrication of false/wrong accusation about any person is another atrocious sin. Muslims rely on moral principles for their decision making process, any false/fake news not only harm their mutual relationships in the society also it will lead to misconceptions. The foremost theme is to keep Muslims away from any commotion which intentionally or unintentionally hurts any other person and he has to be in the pang of guilty afterwards. This study identifies the adverse impacts of spreading fake news and how it is prohibited from Islamic evidences. Moreover, a link between decision making and impact of news on it is developed based on the review of existing literature.

scholarly journals Thirty Years of Human Rights Study in the Web of Science Database (1990–2020)

2021 ◽  
Vol 18 (4) ◽  
pp. 2131
Author(s):  
Priscilla Paola Severo ◽  
Leonardo B. Furstenau ◽  
Michele Kremer Sott ◽  
Danielli Cossul ◽  
Mariluza Sott Bender ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Human Rights ◽  
Network Analysis ◽  
Reproductive Health ◽  
Web Of Science ◽  
Health Issues ◽  
Human Beings ◽  
Field Of Study ◽  
The Web

The study of human rights (HR) is vital in order to enhance the development of human beings, but this field of study still needs to be better depicted and understood because violations of its core principles still frequently occur worldwide. In this study, our goal was to perform a bibliometric performance and network analysis (BPNA) to investigate the strategic themes, thematic evolution structure, and trends of HR found in the Web of Science (WoS) database from 1990 to June 2020. To do this, we included 25,542 articles in the SciMAT software for bibliometric analysis. The strategic diagram produced shows 23 themes, 12 of which are motor themes, the most important of which are discussed in this article. The thematic evolution structure presented the 21 most relevant themes of the 2011–2020 period. Our findings show that HR research is directly related to health issues, such as mental health, HIV, and reproductive health. We believe that the presented results and HR panorama presented have the potential to be used as a basis on which researchers in future works may enhance their decision making related to this field of study.

scholarly journals A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020

2021 ◽  
Vol 18 (3) ◽  
pp. 952 ◽  
Author(s):  
Leonardo B. Furstenau ◽  
Bruna Rabaioli ◽  
Michele Kremer Sott ◽  
Danielli Cossul ◽  
Mariluza Sott Bender ◽  
...  
Keyword(s):  
Decision Making ◽  
Network Analysis ◽  
Web Of Science ◽  
Future Research ◽  
Future Trends ◽  
Damage Prevention ◽  
Over Time ◽  
The Web

The COVID-19 pandemic has affected all aspects of society. Researchers worldwide have been working to provide new solutions to and better understanding of this coronavirus. In this research, our goal was to perform a Bibliometric Network Analysis (BNA) to investigate the strategic themes, thematic evolution structure and trends of coronavirus during the first eight months of COVID-19 in the Web of Science (WoS) database in 2020. To do this, 14,802 articles were analyzed, with the support of the SciMAT software. This analysis highlights 24 themes, of which 11 of the more important ones were discussed in-depth. The thematic evolution structure shows how the themes are evolving over time, and the most developed and future trends of coronavirus with focus on COVID-19 were visually depicted. The results of the strategic diagram highlight ‘CHLOROQUINE’, ‘ANXIETY’, ‘PREGNANCY’ and ‘ACUTE-RESPIRATORY-SYNDROME’, among others, as the clusters with the highest number of associated citations. The thematic evolution. structure presented two thematic areas: “Damage prevention and containment of COVID-19” and “Comorbidities and diseases caused by COVID-19”, which provides new perspectives and futures trends of the field. These results will form the basis for future research and guide decision-making in coronavirus focused on COVID-19 research and treatments.

scholarly journals Active Participation of E-Consumer: A Qualitative Analysis From Fashion Retailer Perspective

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402097916
Author(s):  
Carlota Lorenzo-Romero ◽  
María-Encarnación Andrés-Martínez ◽  
María Cordente-Rodríguez ◽  
Miguel Ángel Gómez-Borja
Keyword(s):  
Decision Making ◽  
Qualitative Research ◽  
Theoretical Model ◽  
Qualitative Analysis ◽  
Social Networking ◽  
Innovation Strategy ◽  
Efficiency And Effectiveness ◽  
Innovation Strategies ◽  
Depth Interviews ◽  
The Web

This work aims to study the web innovation strategies used by Spanish companies in the fashion and accessories sector, with the specific aim of analyzing co-creation as an innovation strategy so that this link with customers will improve efficiency and effectiveness in decision-making. Qualitative research was carried out through in-depth interviews with Spanish professionals and companies in the fashion and accessories sector. Then, a theoretical model was proposed. This model integrates value co-creation, social networking, participation, engagement, feedback, and other variables. This qualitative analysis has relevant value for the professional sector because there are many papers from consumers’ perspective; however, studies from the retail sector’s perspective are less common in the literature. This study contributes ideas for the strategy of co-participation with clients to improve the activity and management of fashion companies.

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

scholarly journals Determinant Factors in Personal Decision-Making to Adopt COVID-19 Prevention Measures in Chile

2021 ◽  
Vol 18 (15) ◽  
pp. 8131
Author(s):  
Karina Fernanda Gonzalez ◽  
Maria Teresa Bull ◽  
Sebastian Muñoz-Herrera ◽  
Luis Felipe Robledo
Keyword(s):  
Decision Making ◽  
Decision Model ◽  
Protective Action ◽  
Prevention Measures ◽  
Protective Measures ◽  
Protective Action Decision Model ◽  
Exploratory Survey ◽  
Relevant Variables ◽  
Personal Decision ◽  
Action Decision

The pandemic has challenged countries to develop stringent measures to reduce infections and keep the population healthy. However, the greatest challenge is understanding the process of adopting self-care measures by individuals in different countries. In this research, we sought to understand the behavior of individuals who take self-protective action. We selected the risk homeostasis approach to identify relevant variables associated with the risk of contagion and the Protective Action Decision Model to understand protective decision-making in the pandemic. Subsequently, we conducted an exploratory survey to identify whether the same factors, as indicated in the literature, impact Chile’s adoption of prevention measures. The variables gender, age, and trust in authority behave similarly to those found in the literature. However, socioeconomic level, education, and media do not impact the protection behaviors adopted to avoid contagion. Furthermore, the application of the Protective Action Decision Model is adequate to understand the protective measures in the case of a pandemic. Finally, women have a higher risk perception and adopt more protective measures, and in contrast, young people between 18 and 30 years of age are the least concerned about COVID-19 infection.

Architecture for Decision-Making and Task-Oriented Reasoning for the Web of Trust

2009 ◽  
Author(s):  
Stanislav Ustymenko ◽  
Daniel G. Schwartz ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
Decision Making ◽  
Web Of Trust ◽  
Task Oriented ◽  
The Web ◽  
And Task

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

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