📄

Paper

Multimodal Claim Extraction for Fact-Checking

by Joycelyn Teo arxiv/2604.16311

Free2AITools Nexus Index

36.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 75

Q: Quality 60

Tech Context

Vital Performance —

Automated Fact-Checking (AFC) relies on claim extraction as a first step, yet existing methods largely overlook the multimodal nature of today's misinformation. Social media posts often combine short, informal text with images such as memes, screenshots, and photos, creating challenges that differ from both text-only claim extraction and well-studied multimodal tasks like image captioning or visual question answering. In this work, we present the first benchmark for multimodal claim extractio...

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Paper Information Summary
Entity Passport
Registry ID	2604.16311
License	arXiv
Provider	arxiv

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2604_16311,
  author = {Joycelyn Teo},
  title = {Multimodal Claim Extraction for Fact-Checking Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.16311}},
  note = {Accessed via Free2AITools.}
}

APA Style

Joycelyn Teo. (2026). Multimodal Claim Extraction for Fact-Checking [Paper]. Free2AITools. https://arxiv.org/abs/2604.16311

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 75

Quality (Q) 60

💬 Index Insight

FNI V2.0 for Multimodal Claim Extraction for Fact-Checking: Authority (A:0), Popularity (P:0), Recency (R:75), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Automated Fact-Checking (AFC) relies on claim extraction as a first step, yet existing methods largely overlook the multimodal nature of today's misinformation. Social media posts often combine short, informal text with images such as memes, screenshots, and photos, creating challenges that differ from both text-only claim extraction and well-studied multimodal tasks like image captioning or visual question answering. In this work, we present the first benchmark for multimodal claim extractio..."

❝ Cite Node

@article{Teo2026Multimodal,
  title={Multimodal Claim Extraction for Fact-Checking},
  author={Joycelyn Teo},
  journal={arXiv preprint arXiv:2604.16311},
  year={2026}
}

👥 Collaborating Minds

Joycelyn Teo

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️75RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.CLField

🏷️ Research Topics

image generationmultimodal

📄 Data Source: arXiv ↗

🔄 Updated daily

Source summary: Based on arXiv metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: 2604.16311
slug: 2604.16311
source: arxiv
author: Joycelyn Teo
license: arXiv
tags: arxiv:cs.CL, arxiv:cs.AI, arxiv:cs.SI, multimodal

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null

Data indexed from public sources. Updated daily.