CLASSIFYING FAKE NEWS ARTICLES USING NATURAL LANGUAGE PROCESSING TO IDENTIFY IN-ARTICLE ATTRIBUTION AS A SUPERVISED LEARNING ESTIMATOR
Main Article Content
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.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
M. Balmas, “When Fake News Becomes
Real: Combined Exposure to Multiple News
Sources and Political Attitudes of Inefficacy,
Alienation, and Cynicism,” Communic. Res.,
vol. 41, no. 3, pp. 430–454, 2014.
C. Silverman and J. Singer-Vine, “Most
Americans Who See Fake News Believe It,
New Survey Says,” BuzzFeed News, 06-Dec-
P. R. Brewer, D. G. Young, and M.
Morreale, “The Impact of Real News about
‘“Fake News”’: Intertextual Processes and
Political Satire,” Int. J.Public Opin. Res., vol.
, no. 3, 2013.
D. Berkowitz and D. A. Schwartz,“Miley,
CNN and The Onion,” Journal.Pract., vol. 10,
no. 1, pp. 1–17, Jan. 2016.
C. Kang, “Fake News Onslaught
Targets Pizzeria as Nest of Child-Trafficking,”
New York Times, 21-Nov-2016.
C. Kang and A. Goldman, “In
Washington Pizzeria Attack, Fake News
Brought Real Guns,” New York Times,05-Dec-