An unsupervised framework for tracing textual sources of moral change

• URL: http://arxiv.org/abs/2109.00608v1
• Date: Wed, 1 Sep 2021 20:35:33 GMT
• Title: An unsupervised framework for tracing textual sources of moral change
• Authors: Aida Ramezani, Zining Zhu, Frank Rudzicz, Yang Xu
• Abstract summary: We present a novel framework for tracing textual sources of moral change toward entities through time. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events.
• Score: 17.010859995410556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Morality plays an important role in social well-being, but people's moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events.

Related papers

• EMONA: Event-level Moral Opinions in News Articles [14.898581862558112]
  This paper initiates a new task to understand moral opinions towards events in news articles. We have created a new dataset, EMONA, and annotated event-level moral opinions in news articles.
  arXiv  Detail & Related papers  (2024-04-02T07:57:19Z)
• 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)
• What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations [48.686872351114964]
  Moral or ethical judgments rely heavily on the specific contexts in which they occur. We introduce defeasible moral reasoning: a task to provide grounded contexts that make an action more or less morally acceptable. We distill a high-quality dataset of 1.2M entries of contextualizations and rationales for 115K defeasible moral actions.
  arXiv  Detail & Related papers  (2023-10-24T00:51:29Z)
• Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? [78.3738172874685]
  Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research.
  arXiv  Detail & Related papers  (2023-08-29T15:57:32Z)
• MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions [71.25236662907056]
  A moral dialogue system aligned with users' values could enhance conversation engagement and user connections. We propose a framework, MoralDial, to train and evaluate moral dialogue systems.
  arXiv  Detail & Related papers  (2022-12-21T02:21:37Z)
• ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations [81.70195684646681]
  We present ClarifyDelphi, an interactive system that learns to ask clarification questions. We posit that questions whose potential answers lead to diverging moral judgments are the most informative. Our work is ultimately inspired by studies in cognitive science that have investigated the flexibility in moral cognition.
  arXiv  Detail & Related papers  (2022-12-20T16:33:09Z)
• 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)
• Contextualized moral inference [12.574316678945195]
  We present a text-based approach that predicts people's intuitive judgment of moral vignettes. We show that a contextualized representation offers a substantial advantage over alternative representations.
  arXiv  Detail & Related papers  (2020-08-25T00:34:28Z)
• 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.