Combating Foreign Disinformation On Social Media Study Overview And

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To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other... For more information, visit www.rand.org/about/principles. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors. The dissemination of purposely deceitful or misleading content to target audiences for political aims or economic purposes constitutes a threat to democratic societies and institutions, and is being increasingly recognized as a major security... Disinformation can also be part of hybrid threat activities. This research paper examines findings on the effects of disinformation and addresses the question of how effective counterstrategies against digital disinformation are, with the aim of assessing the impact of responses such as the...

The paper’s objective is to synthetize the main scientific findings on disinformation effects and on the effectiveness of debunking, inoculation, and forewarning strategies against digital disinformation. A mixed methodology is used, combining qualit... Social media have democratized communication but have led to the explosion of the socalled "fake news" phenomenon. This problem has visible implications on global security, both political (e.g.the QANON case) and health (anti-Covid vaccination and No-Vax fake news). Models that detect the problem in real time and on large amounts of data are needed. Digital methods and text classification procedures are able to do this through predictive approaches to identify a suspect message or author.

This paper aims to apply a supervised model to the study of fake news on the Twittersphere to highlight its potential and preliminary limitations. The case study is the infodemic generated on social media during the first phase of the COVID-19 emergency. The application of the supervised model involved the use of a training and testing dataset. The different preliminary steps to build the training dataset are also shown, highlighting, with a critical approach, the challenges of working with supervised algorithms. Two aspects emerge. The first is that it is important to block the sources of bad information, before the information itself.

The second is that algorithms could be sources of bias. Social media companies need to be very careful about relying on automated classification. This edition doesn't have a description yet. Can you add one? Copy and paste this code into your Wikipedia page. Need help?

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required. Read instantly on your browser with Kindle for Web. Using your mobile phone camera - scan the code below and download the Kindle app. How are state adversaries using disinformation on social media to advance their interests? What does the joint force?and the U.S. Air Force in particular?need to be prepared to do in response?

Disinformation campaigns on social media pose a nuanced threat to the United States, but the response remains ad hoc and uncoordinated. This series overview presents recommendations to better prepare for this new age of information warfare. Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

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