# Political Depolarization of News Articles Using Attribute-aware Word Embeddings arXiv: https://arxiv.org/abs/2101.01391 ยท HTML: https://mlsanity.com/paper/2101.01391/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 60 references that are in the map. - First posted on arXiv 5 Jan 2021. ## Builds on (most cited first, 24 of 24) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016) - [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015) - [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018) - [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017) - [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018) - [Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://mlsanity.com/paper/1808.04339/) (2018) - [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018) - [Generating Sentences by Editing Prototypes](https://mlsanity.com/paper/1709.08878/) (2017) - [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://mlsanity.com/paper/1905.05621/) (2019) - [Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://mlsanity.com/paper/1805.05181/) (2018) - [Content preserving text generation with attribute controls](https://mlsanity.com/paper/1811.01135/) (2018) - [Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus](https://mlsanity.com/paper/1903.10671/) (2019) - [Adversarial Text Generation via Feature-Mover's Distance](https://mlsanity.com/paper/1809.06297/) (2018) - [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015) - [Multiple-Attribute Text Style Transfer](https://mlsanity.com/paper/1811.00552/) (2018) - [A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer](https://mlsanity.com/paper/1906.01833/) (2019) - [Structured Content Preservation for Unsupervised Text Style Transfer](https://mlsanity.com/paper/1810.06526/) (2018) - [Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction](https://mlsanity.com/paper/1704.01691/) (2017) - [Adversarial Multi-Criteria Learning for Chinese Word Segmentation](https://mlsanity.com/paper/1704.07556/) (2017) - [Simple dynamic word embeddings for mapping perceptions in the public sphere](https://mlsanity.com/paper/1904.03352/) (2019) - [Sentiment Transfer using Seq2Seq Adversarial Autoencoders](https://mlsanity.com/paper/1804.04003/) (2018) - [Dictionary-Guided Editing Networks for Paraphrase Generation](https://mlsanity.com/paper/1806.08077/) (2018) ## Built on by (most cited first, 9 of 9) - [Mind's Eye: Grounded Language Model Reasoning through Simulation](https://mlsanity.com/paper/2210.05359/) (2022) - [Mitigating Political Bias in Language Models Through Reinforced Calibration](https://mlsanity.com/paper/2104.14795/) (2021) - [Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits](https://mlsanity.com/paper/2301.00355/) (2023) - [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023) - [Non-Parallel Text Style Transfer with Self-Parallel Supervision](https://mlsanity.com/paper/2204.08123/) (2022) - [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023) - [Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models](https://mlsanity.com/paper/2402.02145/) (2024) - [Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation](https://mlsanity.com/paper/2501.01284/) (2025) - [Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation](https://mlsanity.com/paper/2603.05780/) (2026) ## Abstract (arXiv) Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper, we focus on addressing one of the factors that contributes to this polarity, polarized media. We introduce a framework for depolarizing news articles. Given an article on a certain topic with a particular ideological slant (eg., liberal or conservative), the framework first detects polar language in the article and then generates a new article with the polar language replaced with neutral expressions. To detect polar words, we train a multi-attribute-aware word embedding model that is aware of ideology and topics on 360k full-length media articles. Then, for text generation, we propose a new algorithm called Text Annealing Depolarization Algorithm (TADA). TADA retrieves neutral expressions from the word embedding model that not only decrease ideological polarity but also preserve the original argument of the text, while maintaining grammatical correctness. We evaluate our framework by comparing the depolarized output of our model in two modes, fully-automatic and semi-automatic, on 99 stories spanning 11 topics. Based on feedback from 161 human testers, our framework successfully depolarized 90.1% of paragraphs in semi-automatic mode and 78.3% of paragraphs in fully-automatic mode. Furthermore, 81.2% of the testers agree that the non-polar content information is well-preserved and 79% agree that depolarization does not harm semantic correctness when they compare the original text and the depolarized text. Our work shows that data-driven methods can help to locate political polarity and aid in the depolarization of articles.