Lightweight Machine Learning for Classifying Disaster-Related Social Media Posts
Classifying disaster-related social-media posts into humanitarian information labels is challenging because individual messages are typically short and context-limited, humanitarian categories can overlap semantically, and the CrisisBench label distribution is strongly imbalanced. This study investigates whether lightweight lexical machine-learning methods can provide effective and computationally efficient classification under these conditions. Using the CrisisBench v1.0 English humanitarian task, comprising 87,455 records across predefined Train, Development and Test splits, we conduct a controlled comparison of TF-IDF representations and lightweight classifiers, with Macro-F1 as the pr...
| Authors | Chengjun Jiang, Milena Radenkovic |
|---|---|
| Primary category | cs.NI |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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