Deploying Artificial Intelligence to Combat Disinformation Warfare: Identifying and Interdicting Disinformation Attacks Against Cloud-based Social Media Platforms

Cartwright, Barry; Weir, George; Frank, Richard; Padda, Karmvir

Home // International Journal On Advances in Security, volume 12, numbers 3 and 4, 2019 // View article

Deploying Artificial Intelligence to Combat Disinformation Warfare: Identifying and Interdicting Disinformation Attacks Against Cloud-based Social Media Platforms

Authors:
Barry Cartwright
George Weir
Richard Frank
Karmvir Padda

Keywords: media; disinformation warfare; machine learning

Abstract:
Disinformation attacks that make use of Cloud-based social media platforms, and in particular, the attacks orchestrated by the Russian “Internet Research Agency,” before, during and after 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 technological tools that are capable of identifying such attacks in their earliest stages, rather than identifying and responding to them in retrospect. This paper reports on the interim results of an ongoing research project that was sponsored by the Canadian government’s Cyber Security Directorate. The research is being conducted by the International CyberCrime Research Centre (ICCRC) at Simon Fraser University (Canada), in cooperation with the Department of Information and Computer Sciences at the University of Strathclyde (Scotland). Our ultimate objective is the development of a “critical content toolkit,” which will mobilize artificial intelligence to identify hostile disinformation activities in “near-real-time.” Employing the ICCRC’s Dark Crawler, Strathclyde’s Posit Toolkit, Google Brain’s TensorFlow, plus SentiStrength and a short-text classification program known as LibShortText, 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. To date, we have been able to classify posts as “real news” or “fake news” with an accuracy rate of 90.7%, 90.12%, 89.5%, and 74.26% using LibShortText, Posit, TensorFlow and SentiStrength respectively.

Pages: 203 to 222

Publication date: December 30, 2019

Published in: journal

ISSN: 1942-2636