Code and Data Repository for Confidence-Aware Multimodal Learning for Trustworthy Fake News Detection

This repository provides a new framework for trustworthy fake news detection, including the author's developed multi-modal fusion and confidence calibration. Three online datasets are provided to replicate the results in the paper.

Authors

Ying Chen (ORCID: https://orcid.org/0000-0002-0366-131X)
Shoumeng Ge

Publication Details

Journal: INFORMS journal on computing
Published: 2025-05-15
DOI: https://doi.org/10.1287/ijoc.2024.0655.cd
Citations: 1
Primary Topic: Misinformation and Its Impacts
Type: article
Field-Weighted Citation Impact: 4.20

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Code and Data Repository for Confidence-Aware Multimodal Learning for Trustworthy Fake News Detection

Ying Chen, Shoumeng Ge

May 15, 2025

1 citations

INFORMS journal on computing

Misinformation and Its Impacts

4.20

article

Code and Data Repository for Confidence-Aware Multimodal Learning for Trustworthy Fake News Detection

Ying Chen, Shoumeng Ge

May 15, 2025article en

1 citations

Abstract

INFORMS journal on computing

Openalex Percentile: Top 8%

Misinformation and Its Impacts

4.20

DOI Source OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Code and Data Repository for Confidence-Aware Multimodal Learning for Trustworthy Fake News Detection — Ying Chen, Shoumeng Ge · INFORMS journal on computing (2025) | TGRS Research Map | TGRS