---
title: "Identifying Appropriate Support for Propositions in Online User Comments (2014)"
description: "Online user comments often make arguments whose claims are weakly justified or not justified at all. 34 references, cited by 71 papers here."
authors: ["Joonsuk Park", "Claire Cardie"]
year: 2014
venue: "Proceedings of the First Workshop on Argumentation Mining"
source: "https://aclanthology.org/W14-2105/"
doi: "10.3115/v1/w14-2105"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/park-2014-identifying-appropriate-support-propositions-online/"
---
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# Identifying Appropriate Support for Propositions in Online User Comments

**Authors:** [Joonsuk Park](https://md.research.elamur.ai/free/en/authors/joonsuk-park/) and [Claire Cardie](https://md.research.elamur.ai/free/en/authors/claire-cardie/) · **Year:** 2014 · **Venue:** [Proceedings of the First Workshop on Argumentation Mining](https://md.research.elamur.ai/free/en/venues/workshop-on-argumentation-mining/) ([Workshop on Argumentation Mining 2014](https://md.research.elamur.ai/free/en/venues/workshop-on-argumentation-mining/2014/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W14-2105/) · [PDF](https://aclanthology.org/W14-2105.pdf) · [DOI 10.3115/v1/w14-2105](https://doi.org/10.3115/v1/w14-2105) · [OpenAlex](https://openalex.org/W2250287365)

**Citations:** 151 citations (OpenAlex, 2026-10-14) · [71](https://md.research.elamur.ai/free/en/papers/park-2014-identifying-appropriate-support-propositions-online/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Online user comments often make arguments whose claims are weakly justified or not justified at all. Park and Cardie do not discard these implicit arguments. Instead, they label every proposition by the kind of support it would require. UNVERIFIABLE claims need a reason. VERIFIABLE NON-EXPERIENTIAL claims need objective evidence. VERIFIABLE EXPERIENTIAL claims concern the writer's own state or experience, so evidence for them is optional. Once actual support links are detected, these labels could indicate how well an argument is backed.

The authors built a gold-standard corpus from the Regulation Room eRulemaking platform. It contains 9,476 manually segmented sentences and independent clauses from 1,047 comments on two U.S. regulations. A NONARG class covers material such as questions and greetings. They trained SVM classifiers on n-grams plus features aimed at verifiability and experientiality. These models significantly outperformed a unigram baseline, reaching 68.99% macro-averaged F1. The work matters because it moves argument mining from extracting arguments toward assessing whether their support is adequate. This could enable feedback to writers and recommendations for readers.

## Key points
- The data comes from Regulation Room, run by the Cornell eRulemaking Initiative (CeRI). It covers comments on two rules: Airline Passenger Rights (APR, 820 comments) and Home Mortgage Consumer Protection (HMCP, 227 comments).
- The class distribution is heavily skewed:
  - UNVERIF: 6,146
  - VERIFNON: 1,357
  - VERIFEXP: 1,267
  - NONARG: 708 (about 7%)
- Inter-annotator agreement, measured on 30% of the data, was unweighted Cohen's κ = 0.73. Most disagreements involved statements that could be read either as an opinion or as a description of a law.
- The feature groups were:
  - n-grams (UNI, BI);
  - VER, built mostly from subjectivity-detection lexicons plus POS cues such as VBD and VBN;
  - EXP, consisting of only 6 person and tense features (12 with CCT).
- Adding core-clause tags (CCT) brought no significant gain. Over 83% of unigrams were tagged as core anyway, so models with and without CCT relied on practically the same features. In the UNI+BI+VER+EXP model, "should" pointed toward UNVERIF, while first-person and past-tense cues pointed toward VERIFEXP.

## Interesting ideas
- **Label propositions by the support they need, not by subjectivity.** Tying each class directly to the evidence it would require makes the labels usable for downstream assessment. The same framing could suit fact-checking or citation-need detection in other languages.
- **Segment text before annotators see it.** Letting annotators choose proposition spans themselves led to low reliability. Pre-slicing comments into clauses separated the segmentation problem from the labeling problem. This is a cheap way to improve agreement in any span-level annotation project.
- **Use a deliberately lenient definition of objectivity.** A claim counts as verifiable if the quantity being compared is not open to interpretation, even when its threshold is vague. "Food allergies are rare" qualifies because statistics could back it. This kind of practical criterion keeps annotation consistent in fuzzy cases.
- **Treat hedges as invariant.** Wrappers like "Clearly" or "I believe" do not change whether the embedded assertion can be checked. Encoding this rule prevents surface subjectivity markers from overriding the actual content.
- **Check how much data a feature can actually affect before expecting gains.** The CCT negative result came down to coverage: the core/non-core split applied to too few tokens to matter. Doing this check early saves effort on feature engineering.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Joonsuk Park and Claire Cardie. 2014. Identifying Appropriate Support for Propositions in Online User Comments. Proceedings of the First Workshop on Argumentation Mining.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{park2014identifying,
  title = {{Identifying Appropriate Support for Propositions in Online User Comments}},
  author = {Joonsuk Park and Claire Cardie},
  booktitle = {Proceedings of the First Workshop on Argumentation Mining},
  year = {2014},
  doi = {10.3115/v1/w14-2105},
  url = {https://doi.org/10.3115/v1/w14-2105}
}
```

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*Updated 24 October 2026*