Computer-assisted detection and classification of misinformation about climate change

Abstract

A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by 1) developing and validating a comprehensive taxonomy of climate misinformation, 2) conducting the largest content analysis to date on contrarian claims, 3) developing a computational model to accurately detect specific claims, and 4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to construct a detailed history of misinformation over the past 20 years. Our study finds that climate misinformation produced by CTTs and contrarian blogs has focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims.

Rhetoric and frame analysis of ExxonMobil's climate change communications

“…1,2,[17][18][19][20][21][22][23][24][25] Subtler forms of rhetoric and framing, which dominate today's AGW discourse, are only just beginning to receive similar attention. 7,[26][27][28][29] Fossil fuel interests have spent billions of dollars on AGW public affairs, yet their role in perpetuating these narratives is underexplored. 30,31 In this paper, we analyze how ExxonMobil has publicly constructed AGW frames by selectively emphasizing some terms and topics while avoiding others.…”

“…By bringing to bear the mixed-methods of computational linguistics and inductive frame analysis, our results add to (1) analyses of ExxonMobil's public affairs practices, [32][33][34][35][36][37][38][39][40][41][42][43][44] (2) qualitative accounts of the company's AGW communications, 23,[45][46][47][48][49] and (3) the application of discourse and (algorithmic) content analysis to AGW communications by ExxonMobil and the wider climate countermovement. 1,2,[17][18][19]26,27,29,[50][51][52][53][54][55][56][57] A ''distant''that is, quantitative, statistical, and macroscopic-reading of ExxonMobil's AGW communications offers three practical advantages. 58 First, it complements the qualitative and/or manual methodologies previously applied to the AGW communications of ExxonMobil and other fossil fuel interests, and corroborates our prior work, which used manual coding to demonstrate systematic discrepancies between ExxonMobil's private and public AGW communications.…”

Misinformation in Third-party Voice Applications

“…There has been a significant effort towards identifying misinformation on various platforms like Twitter [5], YouTube [22], and Facebook. 8 , with topics ranging from health to climate science [11,28]. Social media providers have often relied on users to report harmful content.…”

Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey

“…Machine learning (ML) has become widely adopted as a strategy for dealing with a variety of cybersecurity issues. Cybersecurity domains particularly suited to ML include: intrusion detection and prevention [1], network traffic analysis [2], malware analysis [3,4], user behaviour analytics [5], insider threat detection [6], social engineering detection [7], spam detection [8], detection of malicious social media usage [9], health misinformation [10], climate misinformation [11], and more generally "Fake News" [12]. These are essentially classification problems.…”