UTILIZING NATURAL LANGUAGE PROCESSING TO CLASSIFY FAKE NEWS ARTICLES: IDENTIFYING IN-ARTICLE ATTRIBUTION AS A SUPERVISED LEARNING ESTIMATOR
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Abstract
There is a growing need for computational tools that can provide insights on the dependability of online content due to the prevalence of false information in widely accessible media channels including social media feeds, news blogs, and online newspapers. In this study, we explore methods for detecting fabricated news stories in real time. There are two sides to our help. We begin by presenting two new datasets for the fake news detection problem, which together span seven distinct news domains. We give many exploratory analyses aimed at discerning linguistic differences between fake and genuine news information, and we discuss the collecting, annotation, and validation procedure in great detail. We then use the results of these experiments to develop reliable false news detectors. Furthermore, we offer evaluations contrasting machine and human detection of bogus news.
The news that circulates through social media networks is a particularly valuable source of information today. It's easy to see why people are so drawn to internet-based news: there's very little effort required, the information is readily available, and it spreads quickly. Since Twitter is one of the most widely used real-time news platforms, it also ranks highly when it comes to the dissemination of news. In the past, gossip has been shown to do significant harm by disseminating false information.
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