TaxonoMap: An Interactive System for the Exploration and Explanation of Unsupervised Large-Scale News Classification

Simon Ott (Autor:in und Vortragende:r), Daria Liakhovets, Mina Schütz, Medina Andresel, Moritz W. Rothmund-Burgwall, Armin Vogl, Heidi Scheichenbauer, Michael Suker, Alexander Schindler

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung

Abstract

Creating analysis reports on events published in open source news data is a tedious task when done manually. Due to the large-scale nature of news data, analysts, such as government officials, often spend unnecessary resources when trying to research news data on a specific topic. In this paper, we present an interactive system for unsupervised classification of
news articles in a dynamic set of hierarchical labels. By providing users with explanations in the form of highlighted words, we enable them to quickly assess the relevance of an article to
a particular topic. We also provide aggregated visualisations to detect emerging events and include several quality-of-life enhancements such as a source rating mechanism and report
generation.
OriginalspracheEnglisch
TitelCBMI 2024 21st International Conference on Content-Based Multimedia Indexing
Seitenumfang4
PublikationsstatusVeröffentlicht - 2024
Veranstaltung21st International Conference on Content-based Multimedia Indexing - Reykjavik University (RU), Reykjavik, Island
Dauer: 18 Sept. 201720 Sept. 2024
https://cbmi2024.org/

Konferenz

Konferenz21st International Conference on Content-based Multimedia Indexing
KurztitelCBMI 2024
Land/GebietIsland
StadtReykjavik
Zeitraum18/09/1720/09/24
Internetadresse

Research Field

  • Multimodal Analytics

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