Detecting and responding to hostile disinformation activities on social media using machine learning and deep neural networks

Barry Cartwright, Richard Frank, George Weir, Karmvir Padda

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Abstract

Disinformation attacks that make use of social media platforms, e.g., the attacks orchestrated by the Russian “Internet Research Agency” during the 2016 U.S. Presidential election campaign and the 2016 Brexit referendum in the U.K., have led to increasing demands from governmental agencies for AI tools that are capable of identifying such attacks in their earliest stages, rather than responding to them in retrospect. This research undertaken on behalf the of the Canadian Armed Forces and Department of National Defence. Our ultimate objective is the development of an integrated set of machine-learning algorithms which will mobilize artificial intelligence to identify hostile disinformation activities in “near-real-time.” Employing The Dark Crawler, the Posit Toolkit, TensorFlow (Deep Neural Networks), plus the Random Forest classifier and short-text classification programs known as LibShortText and LibLinear, we have analyzed a wide sample of social media posts that exemplify the “fake news” that was disseminated by Russia’s Internet Research Agency, comparing them to “real news” posts in order to develop an automated means of classification.
Original languageEnglish
Pages (from-to)15141-15163
Number of pages23
JournalNeural Computing and Applications
Volume34
Issue number18
Early online date9 Jun 2022
DOIs
Publication statusPublished - Sep 2022

Keywords

  • hostile disinformation
  • machine learning
  • deep neural network
  • Internet Research Agency

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