‘Fake news’ or trust in authorities? The problems of uncertainty at a time of medical crisis

2021 ◽  
Vol 13 (2) ◽  
pp. 287-299 ◽  
Author(s):  
Ian Glenn
Keyword(s):  
South African ◽  
Medical Advice ◽  
Policy Recommendations ◽  
Fake News ◽  
News Sources ◽  
Planning Decisions ◽  
Medical Crisis ◽  
Complex Boundaries

This article examines the complex boundaries between ‘fake news’, speculation, hypothesis, gossip and whistleblowing during the COVID-19 pandemic. It shows that apparently authoritative sources and experts gave information or policy recommendations that have turned out to be wrong, sometimes dangerously so, and explores the kinds of bias that enter medical advice and planning decisions. The article then diagnoses a WhatsApp voice-note from a young South African doctor that went viral and was denounced as ‘fake news’ because of obvious errors. This note, however, revealed behind the scenes medical thinking about subjects that professional bodies and authorities usually avoid discussing publicly. In highlighting what apparently authoritative sources omit and distort, the article suggests that journalists should report medical advice, even from authoritative sources, with caution and shows that apparently ‘fake’ news may reveal issues other news sources neglect.

Fake It “Till You Make It”: Debunking Fake News in a Post-Truth America

2019 ◽  
Vol 8 (1) ◽  
pp. 114-133
Keyword(s):  
Social Media ◽  
First Amendment ◽  
Presidential Election ◽  
Political Discourse ◽  
Cutting Edge ◽  
American People ◽  
Fake News ◽  
News Sources ◽  
The Media ◽  
The U.S

Since the 2016 U.S. presidential election, attacks on the media have been relentless. “Fake news” has become a household term, and repeated attempts to break the trust between reporters and the American people have threatened the validity of the First Amendment to the U.S. Constitution. In this article, the authors trace the development of fake news and its impact on contemporary political discourse. They also outline cutting-edge pedagogies designed to assist students in critically evaluating the veracity of various news sources and social media sites.

Fake News Detection Using Deep Learning

2021 ◽  
pp. 440-451
Author(s):  
Varalakshmi Konagala ◽  
Shahana Bano
Keyword(s):  
Deep Learning ◽  
Uncertain Data ◽  
Online News ◽  
Sufficient Information ◽  
Fake News ◽  
Web Based ◽  
News Sources ◽  
N Gram

The engendering of uncertain data in ordinary access news sources, for example, news sites, web-based life channels, and online papers, have made it trying to recognize capable news sources, along these lines expanding the requirement for computational instruments ready to give into the unwavering quality of online substance. For instance, counterfeit news outlets were observed to be bound to utilize language that is abstract and enthusiastic. At the point when specialists are chipping away at building up an AI-based apparatus for identifying counterfeit news, there wasn't sufficient information to prepare their calculations; they did the main balanced thing. In this chapter, two novel datasets for the undertaking of phony news locations, covering distinctive news areas, distinguishing proof of phony substance in online news has been considered. N-gram model will distinguish phony substance consequently with an emphasis on phony audits and phony news. This was pursued by a lot of learning analyses to fabricate precise phony news identifiers and showed correctness of up to 80%.

scholarly journals Comparing the Application of CEA and BCA to Tuberculosis Control Interventions in South Africa

2019 ◽  
Vol 10 (S1) ◽  
pp. 132-153 ◽  
Author(s):  
Thomas Wilkinson ◽  
Fiammetta Bozzani ◽  
Anna Vassall ◽  
Michelle Remme ◽  
Edina Sinanovic
Keyword(s):  
South Africa ◽  
South African ◽  
Methodological Approach ◽  
Value Of Statistical Life ◽  
Policy Recommendations ◽  
Benefit Cost Analysis ◽  
Tb Control ◽  
Statistical Life ◽  
Benefit Cost ◽  
The Impact

Achieving ambitious targets to address the global tuberculosis (TB) epidemic requires consideration of the impact of competing interventions for improved identification of patients with TB. Cost-effectiveness analysis (CEA) and benefit-cost analysis (BCA) are two approaches to economic evaluation that assess the costs and effects of competing alternatives. However, the differing theoretical basis and methodological approach to CEA and BCA is likely to result in alternative analytical outputs and potentially different policy interpretations. A BCA was conducted by converting an existing CEA on various combinations of TB control interventions in South Africa using a benefits transfer approach to estimate the value of statistical life (VSL) and value of statistical life year (VSLY). All combinations of interventions reduced untreated active disease compared to current TB control, reducing deaths by between 5,000 and 75,000 and resulting in net benefits of Int$3.2–Int$137 billion (ZAR18.1 billion to ZAR764 billion) over a 20-year period. This analysis contributes to development and application of BCA methods for health interventions and demonstrates that further investment in TB control in South Africa is expected to yield significant benefits. Further work is required to guide the appropriate analytical approach, interpretation and policy recommendations in the South African policy perspective and context.

scholarly journals Fake News Detection on Social Media Using A Natural Language Inference Approach

2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi
Keyword(s):  
Social Media ◽  
Natural Language ◽  
News Item ◽  
Support Vector ◽  
Statistical Features ◽  
Fake News ◽  
K Nearest Neighbors ◽  
News Sources ◽  
Online Social Media ◽  
Embedding Methods

Abstract Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

scholarly journals Property rights, institutional change and development in South Africa

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Stefan Schirmer
Keyword(s):  
South Africa ◽  
Property Rights ◽  
Institutional Change ◽  
South African ◽  
Institutional Economics ◽  
Institutional Changes ◽  
Policy Recommendations ◽  
Universal System ◽  
The Past ◽  
Shed Light

Orientation: This article examined the link between property rights and development in the context of South Africa. Research purpose: The article sought to unpack the implications of Hernando De Soto’s work and the broader institutional economics literature for the policy challenges that South Africa currently confronts. Motivation for the Study: Hernando De Soto’s call for a property rights system accessible to all has had a limited impact in South Africa even though his arguments linking poverty to limited property rights systems seems highly relevant here. This is a legacy of Apartheid that has not yet been properly tackled. At the same time, South African realities may raise questions about De Soto’s conclusions and his policy recommendations. Research design: The article provided a textual analysis of De Soto’s work and then applied it to an investigation of South African poverty and the policies that have been implemented since 1994. The article also drew on seminal contributions to institutional economics to shed light on the process of institutional change, and then showed how this perspective fits with much of what De Soto has written about transforming property rights systems. Main findings: This article argued that extending property rights to all is vital for development and for overcoming a major legacy of apartheid. However, moving from a restricted to a universal system requires fundamental institutional changes that are difficult to achieve. Contribution: While De Soto has often advocated a top-down, overly simplistic policy approach in the past, this article showed that the necessary changes can only come about via an incremental, bottom-up approach. To this end, it is particularly important to strengthen the accountability and capacity of local government.

scholarly journals Source Information Affects Interpretations of the News across Multiple Age Groups in the United States

Societies ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 119
Author(s):  
Robert B. Michael ◽  
Mevagh Sanson
Keyword(s):  
United States ◽  
The United States ◽  
Heuristic Approach ◽  
Mechanical Turk ◽  
Political Affiliation ◽  
Fake News ◽  
Source Information ◽  
News Content ◽  
News Sources ◽  
University Of Louisiana

People have access to more news from more sources than ever before. At the same time, they increasingly distrust traditional media and are exposed to more misinformation. To help people better distinguish real news from “fake news,” we must first understand how they judge whether news is real or fake. One possibility is that people adopt a relatively effortful, analytic approach, judging news based on its content. However, another possibility—consistent with psychological research—is that people adopt a relatively effortless, heuristic approach, drawing on cues outside of news content. One such cue is where the news comes from: its source. Beliefs about news sources depend on people’s political affiliation, with U.S. liberals tending to trust sources that conservatives distrust, and vice versa. Therefore, if people take this heuristic approach, then judgments of news from different sources should depend on political affiliation and lead to a confirmation bias of pre-existing beliefs. Similarly, political affiliation could affect the likelihood that people mistake real news for fake news. We tested these ideas in two sets of experiments. In the first set, we asked University of Louisiana at Lafayette undergraduates (Experiment 1a n = 376) and Mechanical Turk workers in the United States (Experiment 1a n = 205; Experiment 1b n = 201) to rate how “real” versus “fake” a series of unfamiliar news headlines were. We attributed each headline to one of several news sources of varying political slant. As predicted, we found that source information influenced people’s ratings in line with their own political affiliation, although this influence was relatively weak. In the second set, we asked Mechanical Turk workers in the United States (Experiment 2a n = 300; Experiment 2b n = 303) and University of Louisiana at Lafayette undergraduates (Experiment 2b n = 182) to watch a highly publicized “fake news” video involving doctored footage of a journalist. We found that people’s political affiliation influenced their beliefs about the event, but the doctored footage itself had only a trivial influence. Taken together, these results suggest that adults across a range of ages rely on information other than news content—such as how they feel about its source—when judging whether news is real or fake. Moreover, our findings help explain how people experiencing the same news content can arrive at vastly different conclusions. Finally, efforts aimed at educating the public in combatting fake news need to consider how political affiliation affects the psychological processes involved in forming beliefs about the news.

scholarly journals Building a Dataset for Detecting Fake News in Amharic Language

2021 ◽  
pp. 76-83
Author(s):  
Tewodros Tazeze ◽  
Raghavendra R
Keyword(s):  
Social Media ◽  
Information Exchange ◽  
Detection System ◽  
Fake News ◽  
News Sources ◽  
Vernacular Language ◽  
Semitic Language ◽  
Media Platform ◽  
Tremendous Challenge ◽  
High Range

The rapid growth and expansion of social media platform has filled the gap of information exchange in the day to day life. Apparently, social media is the main arena for disseminating manipulated information in a high range and exponential rate. The fabrication of twisted information is not limited to ones language, society and domain, this is particularly observed in the ongoing COVID-19 pandemic situation. The creation and propagation of fabricated news creates an urgent demand for automatically classification and detecting such distorted news articles. Manually detecting fake news is a laborious and tiresome task and the dearth of annotated fake news dataset to automate fake news detection system is still a tremendous challenge for low-resourced Amharic language (after Arabic, the second largely spoken Semitic language group). In this study, Amharic fake news dataset are crafted from verified news sources and various social media pages and six different machine learning classifiers Naïve bays, SVM, Logistic Regression, SGD, Random Forest and Passive aggressive Classifier model are built. The experimental results show that Naïve bays and Passive Aggressive Classifier surpass the remaining models with accuracy above 96% and F1- score of 99%. The study has a significant contribution to turn down the rate of disinformation in vernacular language.

scholarly journals Will the crowd game the algorithm? Using layperson judgments to combat misinformation on social media by downranking distrusted sources

2019 ◽  
Author(s):  
Ziv Epstein ◽  
Gordon Pennycook ◽  
David Gertler Rand
Keyword(s):  
Social Media ◽  
Political Polarization ◽  
Survey Experiment ◽  
Fake News ◽  
News Sources ◽  
Ranking Algorithms ◽  
Social Media Platforms ◽  
Highly Correlated ◽  
High Level ◽  
Survey Responses

How can social media platforms fight the spread of misinformation? One possibility is to use newsfeed algorithms to downrank content from sources that users rate as untrustworthy. But will laypeople unable to identify misinformation sites due to motivated reasoning or lack of expertise? And will they “game” this crowdsourcing mechanism to promote content that aligns with their partisan agendas? We conducted a survey experiment in which N = 984 Americans indicated their trust in numerous news sites. Half of the participants were told that their survey responses would inform social media ranking algorithms - creating a potential incentive to misrepresent their beliefs. Participants trusted mainstream sources much more than hyper-partisan or fake news sources, and their ratings were highly correlated with professional fact-checker judgments. Critically, informing participants that their responses would influence ranking algorithms did not diminish this high level of discernment, despite slightly increasing the political polarization of trust ratings.

scholarly journals THE IMPLEMENTATION OF NEW NORMAL POLICIES DURING THE COVID-19 PANDEMIC IN KENDARI CITY, SOUTHEAST SULAWESI, INDONESIA: KNOWLEDGE, ATTITUDES AND PRACTICE

2020 ◽  
Vol 6 (3) ◽  
pp. 99-103
Author(s):  
Juminten Saimin ◽  
Nur Indah Purnamasari ◽  
Hartati Hartati
Keyword(s):  
Descriptive Study ◽  
Positive Thinking ◽  
Fake News ◽  
New Normal ◽  
Social Distancing ◽  
News Sources ◽  
Adequate Knowledge ◽  
Many Sources ◽  
And Behavior ◽  
Attitudes And Behavior

Background: The new normal policy during the COVID-19 pandemic requires public participation. Efforts to suppress the number of cases require knowledge, attitudes and behavior towards health protocols to prevent COVID-19.Objective: This study aimed to assess knowledge, attitudes and practice towards the prevention of COVID-19.Method: This was a descriptive study which included 409 respondents in Kendari City Indonesia conducted in July-August 2020. Data were collected through online questionnaires with google forms.Results: Most of the respondents knew the causes of COVID-19 (85.1%), mode of transmission (65.0%), prevention with masks (96.4%), washing hands (90.5%), social distancing (98.1%), and cough etiquette (80.4%). The attitudes towards public opinions varied. The behaviors towards the prevention of COVID-19 were reading (90.2%), protecting themselves (94.4%), positive thinking (96.1%), doing activities at home (87.3%) and social distancing (93.2%). The behavior to avoid fake news was to ensure the news sources (72.6%), accessed official news (85.5%) and accessed many sources (73.8%).Conclusions: The community of Kendari City has adequate knowledge, attitudes and practice towards prevention of COVID-19. This is a potential asset to implementing the new normal policy during the COVID-19 pandemic.

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