# Identifying Morality Frames in Political Tweets using Relational Learning

arXiv: https://arxiv.org/abs/2109.04535 · HTML: https://mlsanity.com/paper/2109.04535/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Hinge-Loss Markov Random Fields and Probabilistic Soft Logic](https://mlsanity.com/paper/1505.04406/) (2015)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Argument Mining with Structured SVMs and RNNs](https://mlsanity.com/paper/1704.06869/) (2017)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Globally Normalized Transition-Based Neural Networks](https://mlsanity.com/paper/1603.06042/) (2016)
- [Acquiring Background Knowledge to Improve Moral Value Prediction](https://mlsanity.com/paper/1709.05467/) (2017)
- [Modeling Content and Context with Deep Relational Learning](https://mlsanity.com/paper/2010.10453/) (2020)
- [Structured Training for Neural Network Transition-Based Parsing](https://mlsanity.com/paper/1506.06158/) (2015)
- [Entity-Centric Contextual Affective Analysis](https://mlsanity.com/paper/1906.01762/) (2019)
- [Text-based inference of moral sentiment change](https://mlsanity.com/paper/2001.07209/) (2020)
- [Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia](https://mlsanity.com/paper/2010.10820/) (2020)
- [Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis](https://mlsanity.com/paper/1805.02823/) (2018)
- [Randomized Deep Structured Prediction for Discourse-Level Processing](https://mlsanity.com/paper/2101.10435/) (2021)

## Built on by (most cited first, 26 of 26)

- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections](https://mlsanity.com/paper/2305.05094/) (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)
- [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)
- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Towards Few-Shot Identification of Morality Frames using In-Context Learning](https://mlsanity.com/paper/2302.02029/) (2023)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [EMONA: Event-level Moral Opinions in News Articles](https://mlsanity.com/paper/2404.01715/) (2024)
- [Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation](https://mlsanity.com/paper/2406.07935/) (2024)
- ["A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction](https://mlsanity.com/paper/2310.07155/) (2023)
- [Talking Point based Ideological Discourse Analysis in News Events](https://mlsanity.com/paper/2504.07400/) (2025)
- [Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection](https://mlsanity.com/paper/2412.11745/) (2024)
- [Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News](https://mlsanity.com/paper/2402.14224/) (2024)
- [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024)
- [Mapping the Course for Prompt-based Structured Prediction](https://mlsanity.com/paper/2508.15090/) (2025)
- [The Moralization Corpus: Frame-Based Annotation and Analysis of Moralizing Speech Acts across Diverse Text Genres](https://mlsanity.com/paper/2512.15248/) (2025)
- [Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media](https://mlsanity.com/paper/2502.01991/) (2025)
- [News is More than a Collection of Facts: Moral Frame Preserving News Summarization](https://mlsanity.com/paper/2504.00657/) (2025)
- [Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising](https://mlsanity.com/paper/2510.15125/) (2025)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [Effects of Collaboration on the Performance of Interactive Theme Discovery Systems](https://mlsanity.com/paper/2408.09030/) (2024)
- [Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs](https://mlsanity.com/paper/2408.09742/) (2024)
- [Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance](https://mlsanity.com/paper/2607.13260/) (2026)

## Abstract (arXiv)

Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.