# Acquiring Background Knowledge to Improve Moral Value Prediction

arXiv: https://arxiv.org/abs/1709.05467 · HTML: https://mlsanity.com/paper/1709.05467/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2017.

## Built on by (most cited first, 22 of 22)

- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values](https://mlsanity.com/paper/2310.07629/) (2023)
- [NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly](https://mlsanity.com/paper/2210.08604/) (2022)
- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [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)
- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
- [Learning to Adapt Domain Shifts of Moral Values via Instance Weighting](https://mlsanity.com/paper/2204.07603/) (2022)
- [A computational framework for human values](https://mlsanity.com/paper/2305.02748/) (2023)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning](https://mlsanity.com/paper/2401.17228/) (2024)
- [Using Persuasive Writing Strategies to Explain and Detect Health Misinformation](https://mlsanity.com/paper/2211.05985/) (2022)
- [An unsupervised framework for tracing textual sources of moral change](https://mlsanity.com/paper/2109.00608/) (2021)
- ["More Than Words": Linking Music Preferences and Moral Values Through Lyrics](https://mlsanity.com/paper/2209.01169/) (2022)
- [Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments](https://mlsanity.com/paper/2312.03726/) (2023)
- [Detecting value-expressive text posts in Russian social media](https://mlsanity.com/paper/2312.08968/) (2023)
- [An exploration of features to improve the generalisability of fake news detection models](https://mlsanity.com/paper/2502.20299/) (2025)
- [Human Values in Multiagent Systems](https://mlsanity.com/paper/2305.02739/) (2023)
- [Generic Embedding-Based Lexicons for Transparent and Reproducible Text Scoring](https://mlsanity.com/paper/2411.00964/) (2024)
- [Visual moral inference and communication](https://mlsanity.com/paper/2504.11473/) (2025)
- [Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts](https://mlsanity.com/paper/2606.22771/) (2026)
- [ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation](https://mlsanity.com/paper/2609.00057/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background knowledge from an external knowledge base to enrich input texts and thus improve moral value prediction. By combining basic text features with background knowledge, our overall context-aware framework achieves performance comparable to a single human annotator. To the best of our knowledge, this is the first attempt to incorporate background knowledge for the prediction of implicit psychological variables in the area of computational social science.