Work

How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study

Year: 2020

Type: conference-paper

Abstract: We empirically study the effectiveness of machine-generated fake news detectors by understanding the model's sensitivity to different synthetic perturbations during test time.The current machine-generated fake news detectors rely on provenance to determine the veracity of news.Our experiments find that the success of these detectors can be limited since they are rarely sensitive to semantic perturbations and are very sensitive to syntactic perturbations.Also, we would like to open-source our code and believe it could be a useful diagnostic tool for evaluating models aimed at fighting machine-generated fake news.

Source: Workshop on Insights from Negative Results in NLP (Insights)

DOI: 10.18653/v1/2020.insights-1.7

Authors: Meghana Moorthy Bhat, Srinivasan Parthasarathy

Institution: The Ohio State University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response