A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles

By Ruibo Liu, Chenyan Jia, Soroush Vosoughi. 2021

34 citations recorded by the source corpus.

People often prefer to consume news with similar political predispositions and access like-minded news articles, which aggravates polarized clusters known as "echo chamber". To mitigate this phenomenon, we propose a computer-aided solution to help combat extreme political polarization. Specifically, we present a framework for reversing or neutralizing the political polarity of news headlines and articles. The framework leverages the attention mechanism of a Transformer-based language model to first identify polar sentences, and then either flip the polarity to the neutral or to the opposite through a GAN network. Tested on the same benchmark dataset, our framework achieves a 3%-10% improvement on the flipping/neutralizing success rate of headlines compared with the current state-of-the-art model. Adding to prior literature, our framework not only flips the polarity of headlines but also extends the task of polarity flipping to full-length articles. Human evaluation results show that our model successfully neutralizes or reverses the polarity of news without reducing readability. We release a large annotated dataset that includes both news headlines and full-length articles with polarity labels and meta-data to be used for future research. Our framework has a potential to be used by social scientists, content creators and content consumers in the real world.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

A Recipe for Arbitrary Text Style Transfer with Large Language Models (2021, background): “…they can perform a variety of NLP tasks by expressing the task as a prompt that encourages the model to output the desired answer as the continuation (Puri and Catan-zaro, 2019; Weller et al., 2020; Brown et al., 2020; Schick and Schütze, 2021, inter alia ; see Liu et al. (2021a) for a survey).”
Mitigating Political Bias in Language Models Through Reinforced Calibration (2021, methodology): “We used a distribution-balanced version from our prior work (Liu, Jia, and Vosoughi 2021; Liu et al. 2021) (N ≈ 120k, balanced) for better classiﬁer performance and further split the data into training, validation, and test sets by the ratio { 70%, 15%, 15% } , maintaining the original class…”
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2021, methodology): “To improve dialogue safety, numerous work researches on generation detoxifying (Dinan et al., 2019; Smith et al., 2020a; Liu et al., 2021a).”