Fake News Detection Literature Review

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Fake News Detection
Introduction

How can fake news be detected and prevented from dominating the online discourse of news events? Numerous researchers have been discussing this issue and identifying ways to detect fake news, whether on social media (Shu et al.) or by creating a benchmark dataset to facilitate the process (Wang). The topic of this study is fake news detection and what methods are available in this new field. The reason for addressing this topic is that fake news has been a hot button issue in politics ever since the election of Donald Trump. Understanding how fake new proliferates and what can be done to stop its proliferation is something that the digital community can benefit from. The inquiry question for this review is: What are some of the ways that fake news detection can be facilitated?

Body

This literature is organized according to what the researchers have found. The themes include: 1) how fake news is characterized, 2) how fake news is detected, and 3) how fake news proliferation can be prevented. For this literature review, five articles were selected for review. The articles were sorted into common themes based on the results by identifying the main ideas that each presented and then grouping them together into categories based on their commonality. The main themes that they all shared were characterizations of fake new, methods of detection, and the possibility of prevention of proliferation.

How Fake News is Characterized

Fake news has been linked with traditional media outlets—such as CNN and Fox News—but it has also been found to proliferate on social media (Shu et al.). For Conroy, Rubin and Chen, “Fake news detection” is defined as “the task of categorizing news along a continuum of veracity, with an associated measure of certainty” (1). They determined that veracity (truthfulness) is damaged when intentional deceptions are put out in the media. Because the nature of digital media and online news publication is so rapid, there is no mechanism in place for fact checking or for vetting, which means the digital sphere is full of misleading content and fake news. For Rubin, Chen and Conroy, fake news is characterized as deceptive news and phony press releases and hoaxes that are disconnected from their original source material and the appropriate contexts that can help them...…is necessary to use a hybrid approach with machine learning, network-based behavioral data and filtering and vetting methods. However, as the researchers all indicate, fake news detection is very complicated and human intervention is likely to be required even in the future. Thus, the research suggests that fake news detection is much trickier than one might think and even though algorithms and machine learning can play a part in applying predictive modeling, they will not be completely 100% effective. The trick will be, therefore, to learn from the processes that are being created and implemented now so that human intervention and monitoring can be minimized. By relying wholly on the assistance machine learning and linguistic cueing, developers may have a leg up on fake news detection, but there will still be a need for human intelligence. Moreover, this brings to the fore the issue of subjective interference, which could cause news to be mischaracterized as fake—which opens up a problem of moral culpability that still needs to be addressed, as none of these studies took up that particular aspect of this issue in any great or fundamental detail.

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