Publication2026
Multi-Task Disaster Tweet Classification Using Hybrid TF-IDF and Graph Convolutional Networks
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- International — held abroad
- Journal
- Computers Materials and Continua
- Volume
- 87
- Issue
- 2
- Published
- 1 January 2026
- DOI
- 10.32604/cmc.2026.073486
- Scopus ID
- 105032514454
- ISSN
- 15462218
- Authors
- 7
- Open access
- Yes
- Citations
- 0
- International co-authorship
- Yes
- Co-author countries
- IN, SA
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
13 Take urgent action to combat climate change and its impacts
Multi-task disaster tweet classification with 94.47% accuracy enables rapid identification of actionable content for effective disaster response and emergency management.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Graph convolutional networks and TF-IDF integration for disaster detection represents advanced technology application in crisis informatics.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Real-time disaster response technology using social media analysis supports public safety and emergency management decision-making systems.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
Evidence
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Provenance
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Multi-Task Disaster Tweet Classification Using Hybrid TF-IDF and Graph Convolutional Networks”. Kortex.