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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.
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.
Originalsprache | Englisch |
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Titel | CBMI 2024 21st International Conference on Content-Based Multimedia Indexing |
Seitenumfang | 4 |
Publikationsstatus | Veröffentlicht - 2024 |
Veranstaltung | 21st International Conference on Content-based Multimedia Indexing - Reykjavik University (RU), Reykjavik, Island Dauer: 18 Sept. 2017 → 20 Sept. 2024 https://cbmi2024.org/ |
Konferenz
Konferenz | 21st International Conference on Content-based Multimedia Indexing |
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Kurztitel | CBMI 2024 |
Land/Gebiet | Island |
Stadt | Reykjavik |
Zeitraum | 18/09/17 → 20/09/24 |
Internetadresse |
Research Field
- Multimodal Analytics
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A hybrid approach to Disinformation Detection
Schindler, A. (Vortragender)
26 März 2024Aktivität: Vortrag ohne Tagungsband / Vorlesung › Eingeladener Vortrag bei einer wissenschaftlichen Konferenz oder Institution