Assessing online media reliability: assigning a trust metric value and detecting 'Fake News' (Conference Presentation)

Nicholas Snell; Terry Traylor; Jeremy Straub

doi:10.1117/12.2519915

14 May 2019 Assessing online media reliability: assigning a trust metric value and detecting 'Fake News' (Conference Presentation)

Nicholas Snell, Terry Traylor, Jeremy Straub

Author Affiliations +

Proceedings Volume 11011, Cyber Sensing 2019; 110110C (2019) https://doi.org/10.1117/12.2519915
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Today fabricated information is easily distributed throughout social media platforms and the internet, allowing embellished information to effortlessly slip through, misinform and manipulate the public to an attacker's erroneous execution. Falsified information –also known as "fake news" -- has been around for many centuries, but today it presents a unique challenge because it can affect voting patterns, political careers, new business product roll-outs, and countless other information consumption processes. This paper proposes a method that uses machine learning, and Bayes' theorem to identify “Fake News” stories. We use Bayesian estimators to calculate the conditional probability that a story is fake given the presence of feature predictors inside a news story. We present a concise summary of the qualitative methods used to study Fake News stories followed by the Computational Social Science and Machine Learning methods used to train and tune a classifier to detect Fake News. We expose some of the main linguistic trends identified in social media platforms associated with Fake News. We close the paper proposing a larger integrated system that can be used to identify and autonomously archive falsified content.

Conference Presentation

Citation Download Citation

Nicholas Snell, Terry Traylor, and Jeremy Straub "Assessing online media reliability: assigning a trust metric value and detecting 'Fake News' (Conference Presentation)", Proc. SPIE 11011, Cyber Sensing 2019, 110110C (14 May 2019); https://doi.org/10.1117/12.2519915

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