Sign inGet started
Paper · arXiv

When Annotators Agree but Labels Disagree: The Projection Problem in Stance Detection

Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral. This convention was inherited from debate analysis and has been applied without modification to social media since SemEval-2016. However, attitudes toward complex targets are not unitary. A person can accept climate science while opposing carbon taxes, expressing support on one dimension and opposition on another. When annotators must compress such multi-dimensional attitudes into a single label, different annotators may weight different dimensions, producing disagreement that reflects different compression choices rather than confusion. We call this the projection problem. We conduct an anno...

AuthorsBowen Zhang
Primary categorycs.CL
Published25 March 2026
Last updated at source27 April 2026
cs.CLcs.SI
Embed this entry
<iframe src="https://silytics.si/embed/item/f429376c-e602-437c-aa79-576ad5717c60" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.