QuTI! Quantifying Text-Image Consistency in Multimodal Documents

TL;DR

This paper introduces QuTI!, a web-based tool that automatically measures the consistency between text and images in multimodal documents, aiding in fact-checking and content exploration.

Contribution

The paper presents a novel system for quantifying text-image relations in multimodal content, supporting misinformation detection and information verification.

Findings

Effective cross-modal relation quantification

Assists in fact-checking and content exploration

Potential to improve credibility assessment

Abstract

The World Wide Web and social media platforms have become popular sources for news and information. Typically, multimodal information, e.g., image and text is used to convey information more effectively and to attract attention. While in most cases image content is decorative or depicts additional information, it has also been leveraged to spread misinformation and rumors in recent years. In this paper, we present a Web-based demo application that automatically quantifies the cross-modal relations of entities (persons, locations, and events) in image and text. The applications are manifold. For example, the system can help users to explore multimodal articles more efficiently, or can assist human assessors and fact-checking efforts in the verification of the credibility of news stories, tweets, or other multimodal documents.