# Moral Framing and Ideological Bias of News

arXiv: https://arxiv.org/abs/2009.12979 · HTML: https://mlsanity.com/paper/2009.12979/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [Whose Emotions and Moral Sentiments Do Language Models Reflect?](https://mlsanity.com/paper/2402.11114/) (2024)
- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research](https://mlsanity.com/paper/2310.13915/) (2023)
- [Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral](https://mlsanity.com/paper/2502.14083/) (2025)
- [To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence](https://mlsanity.com/paper/2109.05325/) (2021)
- [Learning to Adapt Domain Shifts of Moral Values via Instance Weighting](https://mlsanity.com/paper/2204.07603/) (2022)
- [Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media](https://mlsanity.com/paper/2205.12382/) (2022)
- [Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://mlsanity.com/paper/2104.08829/) (2021)
- [Partial Identification of Dose Responses with Hidden Confounders](https://mlsanity.com/paper/2204.11206/) (2022)
- [Noise Audits Improve Moral Foundation Classification](https://mlsanity.com/paper/2210.07415/) (2022)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities](https://mlsanity.com/paper/2402.01091/) (2024)
- [MoVa: Towards Generalizable Classification of Human Morals and Values](https://mlsanity.com/paper/2509.24216/) (2025)
- [Enhancing Stance Classification on Social Media Using Quantified Moral Foundations](https://mlsanity.com/paper/2310.09848/) (2023)
- [Differences in the Moral Foundations of Large Language Models](https://mlsanity.com/paper/2511.11790/) (2025)
- [Detecting Anti-Vaccine Users on Twitter](https://mlsanity.com/paper/2110.11333/) (2021)
- [The Moral Gap of Large Language Models](https://mlsanity.com/paper/2507.18523/) (2025)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines](https://mlsanity.com/paper/2312.08995/) (2023)
- [Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case Study](https://mlsanity.com/paper/2502.02451/) (2025)
- [News is More than a Collection of Facts: Moral Frame Preserving News Summarization](https://mlsanity.com/paper/2504.00657/) (2025)
- [Left, Right, or Center? Evaluating LLM Framing in News Classification and Generation](https://mlsanity.com/paper/2601.05835/) (2026)
- [Filtering Context Mitigates Scarcity and Selection Bias in Political Ideology Prediction](https://mlsanity.com/paper/2302.00239/) (2023)
- [Do Language Models Understand Morality? Towards a Robust Detection of Moral Content](https://mlsanity.com/paper/2406.04143/) (2024)
- [LML: A Novel Lexicon for the Moral Foundation of Liberty](https://mlsanity.com/paper/2407.11862/) (2024)
- [Through the LLM Looking Glass: A Socratic Probing of Donkeys, Elephants, and Markets](https://mlsanity.com/paper/2503.16674/) (2025)
- [ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation](https://mlsanity.com/paper/2609.00057/) (2026)

## Abstract (arXiv)

News outlets are a primary source for many people to learn what is going on in the world. However, outlets with different political slants, when talking about the same news story, usually emphasize various aspects and choose their language framing differently. This framing implicitly shows their biases and also affects the reader's opinion and understanding. Therefore, understanding the framing in the news stories is fundamental for realizing what kind of view the writer is conveying with each news story. In this paper, we describe methods for characterizing moral frames in the news. We capture the frames based on the Moral Foundation Theory. This theory is a psychological concept which explains how every kind of morality and opinion can be summarized and presented with five main dimensions. We propose an unsupervised method that extracts the framing Bias and the framing Intensity without any external framing annotations provided. We validate the performance on an annotated twitter dataset and then use it to quantify the framing bias and partisanship of news.