Viable Threat on News Reading: Generating Biased News Using Natural Language Models

Saurabh Gupta, Hong Huy Nguyen, Junichi Yamagishi, Isao Echizen


Abstract
Recent advancements in natural language generation has raised serious concerns. High-performance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These models are already being used to create fake news. They can also be exploited to generate biased news, which can then be used to attack news aggregators to change their reader’s behavior and influence their bias. In this paper, we use a threat model to demonstrate that the publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation. A subjective evaluation with 80 participants demonstrated that the generated biased news is generally fluent, and a bias evaluation with 24 participants demonstrated that the bias (left or right) is usually evident in the generated articles and can be easily identified.
Anthology ID:
2020.nlpcss-1.7
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
55–65
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpcss-1.7
DOI:
10.18653/v1/2020.nlpcss-1.7
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpcss-1.7.pdf