Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency

This repository contains the News400 dataset introduced in the paper:

Eric Müller-Budack, Jonas Theiner, Sebastian Diering, Maximilian Idahl, and Ralph Ewerth. 2020. Multimodal Analytics for Real-world News using Measures of Cross-modal Entity Consistency. In Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR '20). Association for Computing Machinery, New York, NY, USA, 16–25. DOI: https://doi.org/10.1145/3372278.3390670

Content

• news400.tar.gz:
  • dataset.jsonl containing:
    • Web links to the news texts
    • Web links to the news image
    • Outputs of the named entity recognition and disambiguation (NERD) approach
    • Untampered and tampered entities
  • .jsonl file for each entity type containing the following information for each entity:
    • Wikidata ID
    • Wikidata label
    • Meta information used for tampering
    • Web links to all reference images crawled from Google, Bing, and Wikidata
  • splits for testing and validation
• news400_features.tar.gz:
  • Visual features of the news images for persons, locations, and scenes
  • Visual features of the reference images for persons, locations, and scenes
• news400_wordembeddings.tar.gz: Word embeddings of all nouns in the news texts

Source Code

The source code to reproduce our results as well as download scripts to crawl news texts and images can be found on our GitHub page: https://github.com/TIBHannover/cross-modal_entity_consistency