---
title: "Recognizing Stances in Ideological — Somasundaran 2010"
description: "The paper asks whether opinion analysis can tell which side a writer takes in two-sided ideological debates on the web, such as abortion or gun rights."
authors: ["Swapna Somasundaran", "Janyce Wiebe"]
year: 2010
venue: "Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text"
source: "https://aclanthology.org/W10-0214/"
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/somasundaran-2010-recognizing-stances-ideological-line-debates/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2010](https://md.research.elamur.ai/free/en/papers/2010/) / Recognizing Stances in Ideological On-Line Debates

# Recognizing Stances in Ideological On-Line Debates

**Authors:** [Swapna Somasundaran](https://md.research.elamur.ai/free/en/authors/swapna-somasundaran/), [Janyce Wiebe](https://md.research.elamur.ai/free/en/authors/janyce-wiebe/) · **Year:** 2010 · **Venue:** Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text · **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/W10-0214/) · [PDF](https://aclanthology.org/W10-0214.pdf) · [OpenAlex](https://openalex.org/W2098420572)

**Citations:** 293 citations (openalex-snapshot · 2026-10-14) · [123](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks whether opinion analysis can tell which side a writer takes in two-sided ideological debates on the web, such as abortion or gun rights. The authors distinguish two kinds of subjectivity. Sentiment covers positive or negative evaluations. Arguing covers claims about what is true or what ought to be done. Arguing had no existing lexical resource, so they derive one automatically from the arguing annotations in the MPQA corpus. They extract the leading n-grams that act as arguing "triggers."

Each opinion is then paired with what it is about, producing opinion-target features for a supervised stance classifier. Posts were collected from debate websites, and debate-specific sides were mapped by hand onto domain-level for/against stances. Several systems are compared: sentiment-only, arguing-only, a combination of both, a unigram baseline and a distribution-based baseline. The combined system clearly beats the distribution baseline and slightly outperforms unigrams. Arguing features alone match unigrams, while sentiment alone falls short. Feature inspection suggests the opinion-based features expose the underlying ideological reasoning better than bare words.

## Key points
- The corpus covers six domains collected from online debate sites, each with posts carrying user-provided stance tags:
  - Existence of God and Healthcare are used for development.
  - Gun Rights, Gay Rights, Abortion and Creationism are used for experiments.
- Domain sizes range from 336 posts (Healthcare, 16 debates) to 1186 posts (Gay Rights, 15 debates). The other domains are:
  - Existence of God: 486 posts
  - Gun Rights: 566 posts
  - Abortion: 618 posts
  - Creationism: 729 posts
- The arguing lexicon is built from MPQA corpus (Version 2) positive and negative arguing annotations. It contains n-grams up to trigrams, taken mainly from the start of annotated spans.
- Sentiment clues come from the existing lexicon of Wilson et al. (2005).
- Features take forms like "ap-rights" (positive arguing toward "rights") or "an-gay" (negative arguing toward "gay").
- The Arg+Sent system performs best overall, slightly above the unigram system. Arguing-only is on par with unigrams, and sentiment-only is weaker than unigrams.

## Interesting ideas
- **Splitting annotations into trigger and remainder.** The authors treat the start of each annotated span as the part that signals the opinion type and discard the rest. This makes span-level annotations usable as a general clue lexicon. The same approach could turn other span-annotated corpora into reusable lexicons for languages or categories that lack them.
- **Pairing opinions with their targets.** The polarity of an opinion, or the topic mentioned, is often ambiguous on its own. Combining the two into one feature resolves this, for example negativity toward government versus negativity toward insurance companies. This is a cheap trick for any task where meaning depends on what an evaluation is directed at.
- **Mapping local labels onto shared global labels.** Each debate has its own side labels ("Yes!!", "for", "It has not"), which the authors map manually onto shared domain-level stances. This lets many small, heterogeneously labelled sources be pooled into one dataset, which is useful when scraping forums or polls.
- **Inspecting features to test interpretability.** The authors list the top features per stance to show what each feature type captures, not just to report scores. The analysis shows that similar concepts receive the same polarity, while alternative concepts receive opposite polarity for the same side. Such qualitative checks help justify a richer feature set even when its accuracy gain is modest.
- **Separating development and test domains.** Some topics are reserved for designing the approach and others for evaluation. This reduces the risk of tuning to particular topics and is a good practice for cross-domain classification studies.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

123 papers of our library cite it (of the 140884 whose bibliographies we read); 21 of them have a page here, 102 not yet.

- [Can Large Language Models Address Open-Target Stance Detection?](https://md.research.elamur.ai/free/en/papers/akash-2025-can-large-language-models-address/)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://md.research.elamur.ai/free/en/papers/allaway-2020-zero-shot-stance-detection-dataset/)
- [Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure](https://md.research.elamur.ai/free/en/papers/allen-2014-detecting-disagreement-conversations-pseudo-monologic/)
- [Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection](https://md.research.elamur.ai/free/en/papers/arakelyan-2023-topic-guided-sampling-data-efficient/)
- [Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings](https://md.research.elamur.ai/free/en/papers/barel-2025-acquired-taste-multimodal-stance-detection/)
- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://md.research.elamur.ai/free/en/papers/grimminger-2021-hate-political-opponent-twitter-corpus/)
- [A Retrospective Analysis of the Fake News Challenge Stance-Detection Task](https://md.research.elamur.ai/free/en/papers/hanselowski-2018-retrospective-analysis-fake-news-challenge/)
- [Cross-Domain Label-Adaptive Stance Detection](https://md.research.elamur.ai/free/en/papers/hardalov-2021-cross-domain-label-adaptive-stance/)
- [Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://md.research.elamur.ai/free/en/papers/he-2022-infusing-knowledge-wikipedia-enhance-stance/)
- [Unsupervised stance detection for arguments from consequences](https://md.research.elamur.ai/free/en/papers/kobbe-2020-unsupervised-stance-detection-arguments-consequences/)
- [A New Direction in Stance Detection: Target-Stance Extraction in the Wild](https://md.research.elamur.ai/free/en/papers/li-2023-new-direction-stance-detection-target/)
- [Stance Detection on Social Media with Background Knowledge](https://md.research.elamur.ai/free/en/papers/li-2023-stance-detection-social-media-background/)
- [JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection](https://md.research.elamur.ai/free/en/papers/liang-2022-jointcl-joint-contrastive-learning-framework/)
- [Multi-modal Stance Detection: New Datasets and Model](https://md.research.elamur.ai/free/en/papers/liang-2024-multi-modal-stance-detection-new/)
- [SemEval-2016 Task 6: Detecting Stance in Tweets](https://md.research.elamur.ai/free/en/papers/mohammad-2016-semeval-2016-task-6-detecting/)
- [Linguistic Models for Analyzing and Detecting Biased Language](https://md.research.elamur.ai/free/en/papers/recasens-2013-linguistic-models-analyzing-detecting-biased/)
- [A Dataset for Multi-Target Stance Detection](https://md.research.elamur.ai/free/en/papers/sobhani-2017-dataset-multi-target-stance-detection/)
- [Stance Detection with Hierarchical Attention Network](https://md.research.elamur.ai/free/en/papers/sun-2018-stance-detection-hierarchical-attention-network/)
- [A Corpus for Research on Deliberation and Debate](https://md.research.elamur.ai/free/en/papers/walker-2012-corpus-research-deliberation-debate/)
- [Stance Classification using Dialogic Properties of Persuasion](https://md.research.elamur.ai/free/en/papers/walker-2012-stance-classification-dialogic-properties-persuasion/)
- [Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation](https://md.research.elamur.ai/free/en/papers/wen-2023-zero-shot-few-shot-stance/)

## Cite

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

```bibtex
@inproceedings{somasundaran2010recognizing,
  title = {{Recognizing Stances in Ideological On-Line Debates}},
  author = {Somasundaran, Swapna and Wiebe, Janyce},
  booktitle = {Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text},
  year = {2010},
  url = {https://aclanthology.org/W10-0214/}
}
```

- [← previous Realization of Discourse Relations by Other Means: Alternative Lexicalizations](https://md.research.elamur.ai/free/en/papers/prasad-2010-realization-discourse-relations-other-means/)
- [next → RelaxCor: A Global Relaxation Labeling Approach to Coreference Resolution](https://md.research.elamur.ai/free/en/papers/sapena-2010-relaxcor-global-relaxation-labeling-approach/)
- [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/)

## References

24 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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