Assessing online media reliability: trust, metrics and assessment

Nicholas Snell; Jeremy Straub; Brandon Stoick; Terry Traylor; William Fleck

doi:10.1117/12.2520127

21 June 2019 Assessing online media reliability: trust, metrics and assessment

Nicholas Snell, Jeremy Straub, Brandon Stoick, Terry Traylor, William Fleck

Proceedings Volume 11013, Disruptive Technologies in Information Sciences II; 1101309 (2019) https://doi.org/10.1117/12.2520127
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Nicholas Snell, Jeremy Straub, Brandon Stoick, Terry Traylor, and William Fleck "Assessing online media reliability: trust, metrics and assessment", Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 1101309 (21 June 2019); https://doi.org/10.1117/12.2520127

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