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Proceedings Paper

Assessing online media reliability: trust, metrics and assessment
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Paper Abstract

Fabricated information is easily distributed throughout social media platforms and the internet. This allows incorrect and embellished information to misinform and manipulate the public in service of an attacker's goals. Falsified information – also commonly known as "fake news" – has been around for centuries. In modern day, it presents a unique challenge because of the difficulty of tracing news items origin, when spread electronically. Fake news can affect voting patterns, political careers, businesses’ new product launches, and countless other information consumption processes. This paper proposes a method that uses machine learning to identify “Fake News” stories. The conditional probability that a story is fake is calculated, given the presence of feature predictors inside a news story. A concise summary of the qualitative methods used to study Fake News stories is presented. This is followed by a discussion of computational social science and machine learning methods that can be used to train and tune a classifier to detect fake news. Some of the main linguistic trends, identified in social media platforms, that are associated with fake news are identified. A larger integrated system that can be used to identify and mitigate the impact of falsified content is also proposed.

Paper Details

Date Published: 21 June 2019
PDF: 7 pages
Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 1101309 (21 June 2019); doi: 10.1117/12.2520127
Show Author Affiliations
Nicholas Snell, North Dakota State Univ. (United States)
Jeremy Straub, North Dakota State Univ. (United States)
Brandon Stoick, North Dakota State Univ. (United States)
Terry Traylor, North Dakota State Univ. (United States)
William Fleck, North Dakota State Univ. (United States)

Published in SPIE Proceedings Vol. 11013:
Disruptive Technologies in Information Sciences II
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)

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