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Retrieving evidence to support or refute claims is a core part of automatic fact-checking. Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: either no access to evidence, access to evidence curated by a human fact-checker, or access to evidence published after a claim was made. In this work, we present the first realistic pipeline to check real-world claims by retrieving raw evidence from the web. We restrict our retriever to only search documents available prior to the claim’s making, modeling the realistic scenario of emerging claims. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment. We conduct experiments on complex political claims in the ClaimDecomp dataset and show that the aggregated evidence produced by our pipeline improves veracity judgments. Human evaluation finds the evidence summary produced by our system is reliable (it does not hallucinate information) and relevant to answering key questions about a claim, suggesting that it can assist fact-checkers even when it does not reflect a complete evidence set.
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This content will become publicly available on September 15, 2026
Moral Sparks in Social Media Narratives
Online platforms offer forums with rich, real-world illustrations of moral reasoning. Among these, the r/AmITheAsshole (AITA) subreddit has become a prominent resource for computational research. In AITA, a user (author) describes an interpersonal moral scenario, and other users (commenters) provide moral judgments with reasons for who in the scenario is blameworthy. Prior work has focused on predicting moral judgments from AITA posts and comments. This study introduces the concept of moral sparks—key narrative excerpts that commenters highlight as pivotal to their judgments. Thus, sparks represent heightened moral attention, guiding readers to effective rationales. Through 24,676 posts and 175,988 comments, we demonstrate that research in social psychology on moral judgments extends to real-world scenarios. For example, negative traits (rude) amplify moral attention, whereas sympathetic traits (vulnerable) diminish it. Similarly, linguistic features, such as emotionally charged terms (e.g., anger), heighten moral attention, whereas positive or neutral terms (leisure and bio) attenuate it. Moreover, we find that incorporating moral sparks enhances pretrained language models’ performance on predicting moral judgment, achieving gains in F1 scores of up to 5.5%. These results demonstrate that moral sparks, derived directly from AITA narratives, capture key aspects of moral judgment and perform comparably to prior methods that depend on human annotation or large-scale generative modeling.
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- Award ID(s):
- 2116751
- PAR ID:
- 10638495
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 76 to 85
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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