Towards Reliable Disaster Detection: Comparing Semantic and Heuristic Filters for Multimodal Data

Social media has become a vital source of real-time information in disaster response, yet the semantic inconsistency and noise in user-generated content present major challenges for automated analysis. This study introduces a multimodal filtering and classification framework to improve the reliability of disaster-related social media data. We evaluate two independent filtering strategies: the Tweet Metric Score, based on a linguistic heuristic score, and CLIPScore, a reference-free semantic alignment metric assessing image-text coherence. Using a curated dataset of crisis-related posts, we train multimodal classifiers that integrate CLIP/ResNet-50 and BERT features via intermediate fusion. Experimental results show that CLIPScore-filtered data consistently outperforms both unfiltered and heuristically filtered datasets, achieving 93.47% test accuracy and 93.41% F1-score. These findings confirm that high-quality, semantically aligned data significantly enhances classification performance. Our approach highlights the importance of content alignment in multimodal crisis informatics and provides a scalable solution for improving situational awareness during emergencies.