Skip to content
AI News HubLIVE
Original source2 min read

IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives

Summary

Researchers introduce a social-science-grounded framework covering social identities, ingroup/outgroup viewpoints, and narrative roles, and build IRON-Meme, a human-annotated benchmark of 1,110 political memes. Most memes assign derogatory roles, and identities, roles, and templates are systematically interconnected. Evaluating nine vision-language models shows frontier-scale and reasoning models perform best, accuracy drops for less frequent labels, and template background knowledge does not improve narrative role classification.

SourcearXiv AIAuthor: Gabriella Bollici, Stefano De Giorgis, Wouter Van Atteveldt, Filip Ilievski
IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions
Read article

[Submitted on 22 Oct 2026]

Title:IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives

View a PDF of the paper titled IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives, by Gabriella Bollici and 3 other authors

View PDF HTML (experimental)

Abstract:As polarization grows on social media, understanding how political memes shape online narratives has become increasingly important. Previous research has relied on either qualitative frameworks that are hard to scale or computational methods that lack theoretical grounding. Both approaches treat narrative categories in isolation and focus exclusively on moral portrayals of identity. To address these limitations, we introduce a framework grounded in social science theories, encompassing three categories: social identities (such as gender, politics, and ethnicity), viewpoints (ingroup and outgroup perspectives), and narrative roles (moral character, intelligence, status, and power). Building on this framework, we present IRON-Meme, a human-annotated benchmark comprising 1,110 political memes across substantially more templates than existing benchmarks. Our analysis of IRON-Meme shows that most memes assign derogatory roles and that identities, roles, and templates are systematically interconnected. Evaluation of nine vision-language models indicates that frontier-scale and reasoning models achieve the highest performance, while accuracy on social identities and narrative roles decreases for less frequent labels. Incorporating template background knowledge does not enhance narrative role classification, suggesting that meme interpretation relies on how memes are adapted for specific communicative purposes. We make IRON-Meme and our code publicly available.

Comments: Submitted to ARR

Subjects:

Artificial Intelligence (cs.AI)

MSC classes: 68T07

Cite as: arXiv:2610.33097 [cs.AI]

(or arXiv:2610.33097v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33097

arXiv-issued DOI via DataCite

Submission history

From: Gabriella Bollici [view email] [v1] Thu, 22 Oct 2026 10:51:07 UTC (7,882 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives, by Gabriella Bollici and 3 other authors

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.AI

new | recent | 2026-10

Change to browse by:

cs

References & Citations

SciX

Google Scholar

Semantic Scholar

Loading...

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

Key points and analysis

Article intelligence

InvestorsAdvanced

Key points

  • IRON-Meme is a human-annotated benchmark of 1,110 political memes spanning substantially more templates than existing benchmarks.
  • The framework separates social identities, ingroup/outgroup viewpoints, and narrative roles such as moral character, intelligence, status, and power.
  • Most memes assign derogatory roles, and identities, roles, and templates are systematically interconnected.
  • Frontier-scale and reasoning models lead among nine evaluated vision-language models, but low-frequency labels hurt accuracy, and template background knowledge does not help role classification.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

More recent stories on this topic · Research