scholarly journals ‘Fake news’ is the invention of a liar: How false information circulates within the hybrid news system

2019 ◽  
Vol 67 (4) ◽  
pp. 625-642 ◽  
Author(s):  
Fabio Giglietto ◽  
Laura Iannelli ◽  
Augusto Valeriani ◽  
Luca Rossi
Keyword(s):  
Conceptual Framework ◽  
Fake News ◽  
False Information ◽  
Misleading Information ◽  
Meso Level ◽  
News Stories ◽  
Global Circulation ◽  
Different Types ◽  
Change Of Perspective ◽  
Micro Level

Alarmed by the oversimplifications related to the ‘fake news’ buzzword, researchers have started to unpack the concept, defining diverse types and forms of misleading news. Most of the existing works in the area consider crucial the intent of the content creator in order to differentiate among different types of problematic information. This article argues for a change of perspective that, by leveraging the conceptual framework of sociocybernetics, shifts from exclusive attention to creators of misleading information to a broader approach that focuses on propagators and, as a result, on the dynamics of the propagation processes. The analytical implications of this perspective are discussed at a micro level (criteria to judge the falsehood of news and to decide to spread it), at a meso level (four possible relations between individual judgements and decisions), and at a macro level (global circulation cascades). The authors apply this theoretical gaze to analyse ‘fake news’ stories that challenge existing models.

Varying Degrees of Support: Understanding Parents’ Positive Attitudes Toward LGBT Coaches

2014 ◽  
Vol 28 (4) ◽  
pp. 387-398 ◽  
Author(s):  
George Cunningham ◽  
E. Nicole Melton
Keyword(s):  
United States ◽  
Parental Influences ◽  
Macro Level ◽  
Factors Affecting ◽  
Meso Level ◽  
Cognitive Influences ◽  
Positive Attitudes ◽  
Different Types ◽  
Bisexual And Transgender ◽  
Micro Level

The purpose of this study was to examine parents’ supportive attitudes toward lesbian, gay, bisexual, and transgender (LGBT) coaches, as well as the sources of that support. The authors drew from the model of dual attitudes and a multilevel framework developed for the study to guide the analyses. Interviews were conducted with 10 parents who lived in the southwest United States. Analysis of the data revealed three different types of support: indifference, qualified support, and unequivocal support. Further analyses provided evidence of multilevel factors affecting the support, including those at the macro-level (religion), the meso-level (parental influences and contact with sexual minorities), and the micro-level (affective and cognitive influences) of analysis. Theoretical implications and contributions of the study are discussed.

Fake News

2018 ◽  
Author(s):  
Bente Kalsnes
Keyword(s):  
Communication Technologies ◽  
Social Motivation ◽  
Research Literature ◽  
Fake News ◽  
False Information ◽  
Conspiracy Theories ◽  
Different Types ◽  
Fact Checking ◽  
Definition Of ◽  
Different Levels

Fake news is not new, but the American presidential election in 2016 placed the phenomenon squarely onto the international agenda. Manipulation, disinformation, falseness, rumors, conspiracy theories—actions and behaviors that are frequently associated with the term—have existed as long as humans have communicated. Nevertheless, new communication technologies have allowed for new ways to produce, distribute, and consume fake news, which makes it harder to differentiate what information to trust. Fake news has typically been studied along four lines: Characterization, creation, circulation, and countering. How to characterize fake news has been a major concern in the research literature, as the definition of the term is disputed. By differentiating between intention and facticity, researchers have attempted to study different types of false information. Creation concerns the production of fake news, often produced with either a financial, political, or social motivation. The circulation of fake news refers to the different ways false information has been disseminated and amplified, often through communication technologies such as social media and search engines. Lastly, countering fake news addresses the multitude of approaches to detect and combat fake news on different levels, from legal, financial, and technical aspects to individuals’ media and information literacy and new fact-checking services.

scholarly journals Entrepreneurship and Value Creation: Curriculum at Macro, Meso, and Micro level

2016 ◽  
pp. 19-35
Author(s):  
Muhammad Junaid Et al.,
Keyword(s):  
Strategic Management ◽  
Conceptual Framework ◽  
Value Creation ◽  
Current Practice ◽  
Macro Level ◽  
Generation Model ◽  
Entrepreneurial Process ◽  
Meso Level ◽  
Micro Level ◽  
Institutional Boundaries

The paper advances the idea of entrepreneurship as value creation in a conceptual framework on macro, meso and micro levels with regards to value creation for oneself and others. To bridge together the three conceptual levels to which thus far too little at-tention has been made, the authors first weave together literature on value creation from fields of economics, sociology, Strategic Management and psychology in a framework to generate inputs for macro-level curriculum. The overarching and embedded nature of business generation model allows us to introduce the mechanisms for infusing value creation at meso level to operate within the institutional boundaries of the curriculum in consultation with salient stakeholders. In the same vein, at the micro level, the Harmonized model of the entrepreneurial process which reconciles seemingly con-tending views of Causation, effectuation, and Bricolage is proposed to generate input for micro-level curriculum at the classroom level. Finally, the paper reviews germinal learn-ing theories which afford a roadmap for the transition from the current practice of teaching to the desirable level of value creation based pedagogies.

Survey on Fake News or Truth - Rumours Detection using Machine Learning

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Kalyani Deore ◽  
Leena Gaikwad ◽  
Rohit Dhamne ◽  
Vishal Agale ◽  
T. Bhaskar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Modern World ◽  
Fake News ◽  
Misleading Information ◽  
Original Source ◽  
News Stories ◽  
False News ◽  
Communication Methods

This study is to help readers to understand detection of fake news using machine learning. The main purpose of the planned system is to build an application which identifies fake news stories from a bunch of news stories to make people aware of fake news rumours. With the help of machine learning algorithms, we can detect and separate out the fake news. Nowadays, it is become harder to identify the original source of news stories, like looking for a needle in a haystack. In the modern world, news is a kind of communication that keeps us up to date on the latest events, topics, and people in the wider globe. A society relies on news for a variety of reasons, the most important of which is informing its members about events taking on in and around them that might influence them. Oral and traditional media, as well as digital communication methods, altered videos, memes, unconfirmed marketing, and social media have all contributed to the spread of rumors. As nowadays many people use social media in many cases people get wrong and misleading information and people share those stories without verifying whether it is real or fake news stories. Spreading false information on social media has become a major problem these days. That is why we need a system that can tell us whether something is false news or not. Applications are: 1. Fake news may be detected on social media using this approach. 2. The system can be used to help news channels to broadcast only real and classified news. 3. Users can easily detect and eliminate fake articles that contain misinformation intended to mislead readers.

Fake News

2018 ◽  
Author(s):  
Leticia Bode ◽  
Emily K. Vraga ◽  
Kjerstin Thorson
Keyword(s):  
Social Media ◽  
Presidential Elections ◽  
Fake News ◽  
News Stories ◽  
The World ◽  
Different Types ◽  
2016 Election ◽  
Use Of Social Media ◽  
Insight Into

Chapter 7 tackles the challenges posed by misinformation campaigns and fake news, an issue of growing concern in America and around the world. Following the 2016 U.S. presidential elections, academics and pundits alike struggled to make sense of what happened, and many pointed to the role of fake news and misinformation more broadly in leading voters astray in their assessments of the two major candidates for president. This chapter draws on survey data to investigate how media use in general, and use of social media and partisan media more specifically, affected belief in six fake news stories directly following the 2016 election. The analysis assesses whether use of different types of media affected belief in misinformation—including messages congruent and incongruent with their own candidate preferences—providing insight into what was to blame for belief in fake news in the 2016 elections.

scholarly journals Shades of Fake News: Manifestation, Effects and Ways to Combat False Information

2019 ◽  
Vol 21 (2) ◽  
pp. 41
Author(s):  
Alexandru Cristian Dumitrache
Keyword(s):  
Exploratory Study ◽  
Scientific Literature ◽  
Political Environment ◽  
Ordinary People ◽  
Fake News ◽  
False Information ◽  
Detection Techniques ◽  
Complex Concept ◽  
Different Types ◽  
News Effects

In a continually changing global political environment, fake news has become a widely debated topic by both researchers and ordinary people. Despite the relevance and the diversity of approaches, few studies have focused on the typology of fake news in specialised scientific literature, while proper assessment methods and detection techniques are not well-established yet. This paper addresses the complex concept of fake news, presenting its significance and highlighting its different types, from propaganda to news satire; the moderators of the fake news effects and the ways to counter disinformation. This exploratory study reveals that solutions to combat the phenomenon exist, but they focus more on effects rather than on causes, leaving space open for further research.

scholarly journals Fake News Detection: A Survey of Techniques

2020 ◽  
Vol 9 (9) ◽  
pp. 383-386
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Fake News ◽  
False Information ◽  
News Stories ◽  
Learning Techniques ◽  
Negative Impacts ◽  
To Come

The extensive spread of fake news (low quality news with intentionally false information) has the potential for extremely negative impacts on individuals, society and particular in the political world. Therefore, fake news detection on social media has recently become an emerging research which is attracting tremendous attention. Detection of false information is technically challenging for several reasons. Use of various social media tools, content is easily generated and quickly spread, which lead to a large volume of content to analyze. Online information is very wide spread, which cover a large number of subjects, which contributes complexity to this task. The application of machine learning techniques are explored for the detection of ‘fake news’ that come from non-reputable sources which mislead real news stories. The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information. This paper performs survey of Machine learning techniques which is mainly used for false detection and provides easier way to generate results.

Does Media Literacy Help Identification of Fake News? Information Literacy Helps, but Other Literacies Don’t

2019 ◽  
pp. 000276421986940 ◽  
Author(s):  
S. Mo Jones-Jang ◽  
Tara Mortensen ◽  
Jingjing Liu
Keyword(s):  
Media Literacy ◽  
Information Literacy ◽  
The Self ◽  
Fake News ◽  
Digital Literacies ◽  
Misleading Information ◽  
Literacy Interventions ◽  
News Stories ◽  
Harmful Effects

Concerns over fake news have triggered a renewed interest in various forms of media literacy. Prevailing expectations posit that literacy interventions help audiences to be “inoculated” against any harmful effects of misleading information. This study empirically investigates such assumptions by assessing whether individuals with greater literacy (media, information, news, and digital literacies) are better at recognizing fake news, and which of these literacies are most relevant. The results reveal that information literacy—but not other literacies—significantly increases the likelihood of identifying fake news stories. Interpreting the results, we provide both conceptual and methodological explanations. Particularly, we raise questions about the self-reported competencies that are commonly used in literacy scales.

scholarly journals “Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content

2019 ◽  
pp. 000276421987822 ◽  
Author(s):  
Maria D. Molina ◽  
S. Shyam Sundar ◽  
Thai Le ◽  
Dongwon Lee
Keyword(s):  
Online News ◽  
Citizen Journalism ◽  
Fake News ◽  
Online Content ◽  
False Information ◽  
Message Source ◽  
Different Types ◽  
False News ◽  
Distinguishing Features ◽  
Source Structure

As the scourge of “fake news” continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging “fake news,” we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of “fake news” that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of “fake news” (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with “real news” by introducing a taxonomy of operational indicators in four domains—message, source, structure, and network—that together can help disambiguate the nature of online news content.

Can fake news really change behaviour? Evidence from a study of COVID-19 misinformation.

2020 ◽  
Author(s):  
Ciara Greene ◽  
Gillian Murphy
Keyword(s):  
Empirical Research ◽  
Health Behaviour ◽  
Real World ◽  
Empirical Data ◽  
Contact Tracing ◽  
Fake News ◽  
Behavioural Intentions ◽  
The Real ◽  
News Stories

Previous research has argued that fake news may have grave consequences for health behaviour, but surprisingly, no empirical data have been provided to support this assumption. This issue takes on new urgency in the context of the coronavirus pandemic. In this large preregistered study (N = 3746) we investigated the effect of exposure to fabricated news stories about COVID-19 on related behavioural intentions. We observed small but measurable effects on some related behavioural intentions but not others – for example, participants who read a story about problems with a forthcoming contact-tracing app reported reduced willingness to download the app. We found no effects of providing a general warning about the dangers of online misinformation on response to the fake stories, regardless of the framing of the warning in positive or negative terms. We conclude with a call for more empirical research on the real-world consequences of fake news.

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