The Moral Foundations Weibo Corpus

• URL: http://arxiv.org/abs/2411.09612v1
• Date: Thu, 14 Nov 2024 17:32:03 GMT
• Title: The Moral Foundations Weibo Corpus
• Authors: Renjie Cao, Miaoyan Hu, Jiahan Wei, Baha Ihnaini,
• Abstract summary: Moral sentiments influence both online and offline environments, shaping behavioral styles and interaction patterns. Existing corpora, while valuable, often face linguistic limitations. This corpus consists of 25,671 Chinese comments on Weibo, encompassing six diverse topic areas.
• Score: 0.0
• License:
• Abstract: Moral sentiments expressed in natural language significantly influence both online and offline environments, shaping behavioral styles and interaction patterns, including social media selfpresentation, cyberbullying, adherence to social norms, and ethical decision-making. To effectively measure moral sentiments in natural language processing texts, it is crucial to utilize large, annotated datasets that provide nuanced understanding for accurate analysis and modeltraining. However, existing corpora, while valuable, often face linguistic limitations. To address this gap in the Chinese language domain,we introduce the Moral Foundation Weibo Corpus. This corpus consists of 25,671 Chinese comments on Weibo, encompassing six diverse topic areas. Each comment is manually annotated by at least three systematically trained annotators based on ten moral categories derived from a grounded theory of morality. To assess annotator reliability, we present the kappa testresults, a gold standard for measuring consistency. Additionally, we apply several the latest large language models to supplement the manual annotations, conducting analytical experiments to compare their performance and report baseline results for moral sentiment classification.

Related papers

• Language Model Alignment in Multilingual Trolley Problems [138.5684081822807]
  Building on the Moral Machine experiment, we develop a cross-lingual corpus of moral dilemma vignettes in over 100 languages called MultiTP. Our analysis explores the alignment of 19 different LLMs with human judgments, capturing preferences across six moral dimensions. We discover significant variance in alignment across languages, challenging the assumption of uniform moral reasoning in AI systems.
  arXiv  Detail & Related papers  (2024-07-02T14:02:53Z)
• MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions [4.747987317906765]
  Moral values play a fundamental role in how we evaluate information, make decisions, and form judgements around important social issues. Recent advances in Natural Language Processing (NLP) show that moral values can be gauged in human-generated textual content. This paper introduces MoralBERT, a range of language representation models fine-tuned to capture moral sentiment in social discourse.
  arXiv  Detail & Related papers  (2024-03-12T14:12:59Z)
• Enhancing Stance Classification on Social Media Using Quantified Moral Foundations [7.061680079778037]
  We investigate how moral foundation dimensions can contribute to predicting an individual's stance on a given target. We incorporate moral foundation features extracted from text, along with message semantic features, to classify stances at both message- and user-levels. Preliminary results suggest that encoding moral foundations can enhance the performance of stance detection tasks.
  arXiv  Detail & Related papers  (2023-10-15T14:40:57Z)
• Knowledge of cultural moral norms in large language models [3.475552182166427]
  We investigate the extent to which monolingual English language models contain knowledge about moral norms in different countries. We perform our analyses with two public datasets from the World Values Survey and PEW global surveys on morality. We find that pre-trained English language models predict empirical moral norms across countries worse than the English moral norms reported previously.
  arXiv  Detail & Related papers  (2023-06-02T18:23:35Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [56.85319224208865]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account. We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed. Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• A Corpus for Understanding and Generating Moral Stories [84.62366141696901]
  We propose two understanding tasks and two generation tasks to assess these abilities of machines. We present STORAL, a new dataset of Chinese and English human-written moral stories.
  arXiv  Detail & Related papers  (2022-04-20T13:12:36Z)
• Learning to Adapt Domain Shifts of Moral Values via Instance Weighting [74.94940334628632]
  Classifying moral values in user-generated text from social media is critical to understanding community cultures. Moral values and language usage can change across the social movements. We propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks.
  arXiv  Detail & Related papers  (2022-04-15T18:15:41Z)
• Identifying Morality Frames in Political Tweets using Relational Learning [27.047907641503762]
  Moral sentiment is motivated by its targets, which can correspond to individuals or collective entities. We introduce morality frames, a representation framework for organizing moral attitudes directed at different entities. We propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly.
  arXiv  Detail & Related papers  (2021-09-09T19:48:57Z)
• Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions? [62.74872383104381]
  We investigate the effectiveness of natural language interventions for reading-comprehension systems. We propose a new language understanding task, Linguistic Ethical Interventions (LEI), where the goal is to amend a question-answering (QA) model's unethical behavior.
  arXiv  Detail & Related papers  (2021-06-02T20:57:58Z)
• Aligning AI With Shared Human Values [85.2824609130584]
  We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. We find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
  arXiv  Detail & Related papers  (2020-08-05T17:59:16Z)
• Text-based inference of moral sentiment change [11.188112005462536]
  We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. We build our methodology by exploring moral biases learned from diachronic word embeddings. Our work offers opportunities for applying natural language processing toward characterizing moral sentiment change in society.
  arXiv  Detail & Related papers  (2020-01-20T18:52:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.