The Economics of “Fake News”

False information has economic, political, and social consequences. The authors analyze the real and perceived costs and benefits to those that engage in the creation and platform support of false information. Special consideration is given here to digital advertising ecosystems that provide a supportive environment for “fake news” creation. Fake news is one type of false information. The authors discuss the context of fake-news consumption, and suggest that fake-news creators, consumers, and various arbiters can reinforce each other and form a vicious circle. The article proposes mechanisms to break the circle and alter the cost-benefit structure of engaging in this activity

Social Media and Fake News in the 2016 Election

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories ("fake news"), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online sur-vey, we find:(i) social media was an important but not dominant source of election news, with14 percent of Americans calling social media their "most important" source;(ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared8 million times;(iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and(iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks

Clicks at Any Cost: Why Regulation Won\u27t Upend the Economics of Fake News

Increasingly, the production of fake news has become industrialized; enterprising entrepreneurs are deceiving Internet users with false information while earning significant sums of money. The problem of fake news and misinformation, however, is not one solely brought about by the increase in digital technology. Historically, campaigns of misinformation have been used to achieve social, political, and economic goals long before the Internet was commonplace. But, recent calls to regulate fake news content contravene American law and run afoul of our nation’s laissez-faire approach to the regulation of false or misleading information. We argue that government-imposed, speech-limiting restrictions cannot contain fake news and, as a result, should not be the answer to the modern fake news epidemic. Instead, the key to combatting the effects of fake news lies in a variety of private-sector initiatives and speech-enhancing protocols. Programs designed to reiterate the importance of media literacy and revitalize civic participation are the cornerstone to ensuring a successful democracy in a digital world

Social Media Platforms and Responsibility for Disinformation

Researchers are paying closer attention to the rise of disinformation on social media platforms and what responsibility, if any, the companies that control these platforms have for false information being spread on their websites. In this paper, we highlight the recent growth in concern regarding online disinformation, discuss other works regarding the use of social media as a tool for spreading disinformation, and discuss how coordinated disinformation campaigns on social media platforms are used to spread propaganda and lies about current political events. We also evaluate the reactions of social media platforms in combatting disinformation and the difficulty in policing it. Finally, we argue the point that governments should not have the power to regulate the content of social media platforms except in cases where said content is actively illegal or could be categorized as a type of speech that is not protected by the First Amendment

FAKE NEWS DETECTION ABOUT SARS-COV-2 PANDEMIC USING NEUTRAL NETWORKS AND DETECTION ALGORITHMS

Fake news has an extremely high impact on society, spreading quite simple and fast through social media, TV, internet, press, and other means of communication. The false news about the new coronavirus is blocked by the authorities, according to the decree for establishing a state of emergency. The misinformation of the population and the placement of fake news is two inevitable consequences in times of crisis, these being amplified by two other elements, which feed each other: fear of illness, which can cause deaths, but also uncertainty or lack of information on how to manage the crisis and what is involved.  The need to stop the spreading of fake news, it's paramount and this paper proposes to recognizing truthful information from false information during the pandemic COVID-19 through a guide learning method. This guide implies a model for distinguishing false messages in the online environment, such as Machine Learning algorithms, which can have an accuracy of over 95%

The Making of a President Using Data Analytics and Social Media

Today, political campaigns rely heavily on analytics to target potential voters. The algorithms used, as well as bias that may be introduced in the process, may skew the results and cost an election, as seen in the Clinton campaign. Additionally, Social Media plays a huge role in political races, as does fake news. More alarming is the new trend for Social Media sites to censor anyone or anything which can also make or break a campaign. In this paper we research events that affected the 2016 Presidential election and present issues that may have an effect on future elections

Looking Beyond Content: Modeling and Detection of Fake News from a Social Context Perspective

The widespread fake news on social media has boosted the demand for reliable fake news detection techniques. Such dissemination of fake news can influence public opinions and society. More recently, a growing number of methods for detecting fake news have been proposed. However, most of these approaches have significant limitations in timely detection of fake news. To facilitate early detection of fake news, we propose a unique framework FNEPP (Fake News Engagement and Propagation Path) from a social context perspective, which explicitly combines news contents, user engagements, user characteristics, and the news propagation path as composite features of two collaborative modules. The engagement module captures news contents and user engagements, while the propagation path module learns global and local patterns of user characteristics and news dissemination patterns. Experimental results on two real-world datasets demonstrate the effectiveness and efficiency of the proposed FNEPP framework