## 1 Introduction  
In the era of escalating global crises, social media has become the primary real-time information conduit, generating over 30 million multimodal tweets daily during events like the 2024 Turkey-Syria earthquake [1]. The WHO 2025 report highlights that delayed crisis classification reduces rescue efficiency by 42% within the first 72 h, while misclassification in the 2024 Hawaiian wildfires caused 30% of rescue resources to be misallocated, exposing the urgent need for robust multimodal filtering. As highlighted in [2], 20% of crisis tweets contain noisy text, including misspellings (e.g., \"fload\" instead of \"flood\") or informal abbreviations, which significantly impede accurate classification. Additionally, unlike curated datasets, disaster tweets often exhibit tenuous text-image connections. Research by [3] reveals that 40% of these multimodal posts exhibit weak cross-modal alignment, where text and images often convey inconsistent or unrelated semantic information--as illustrated in Figure 1, the top-left tweet (\"Arizona Task Force 1 Urban Search and Rescue helping #Harvey and now #Irma #AZ\") pairs flood rescue imagery with text conflating Hurricane Harvey and Irma--exemplifying the challenge of fusing incongruent visual and textual semantics. Class imbalance further compounds the issue, with categories such as Hazardous_materials_release constituting merely 4.1% of samples within the CrisisMMD dataset [2]. This urgency is underscored by the WHO's 2025 report, indicating that delayed crisis classification reduces rescue efficiency by 37% during the first 72 h of disasters. Disaster tweet classification spans binary informativeness assessment (e.g., \"informative\" vs. \"non-informative\") and fine-grained multiclass classification (e.g., affected_individuals, rescue_volunteering). Traditional approaches to multimodal crisis tweet classification have evolved from unimodal models, such as BERT for text [4] and ViT for images [5], to static fusion strategies. Early fusion methods, like feature concatenation, achieve an accuracy of 78% but struggle to capture fine-grained text-image interactions [6]. Figure 1: Multimodal tweet instances from the CrisisMMD dataset (text-image pairs). Late fusion frameworks, including CLIP-based contrastive learning, can reach 90.22% accuracy but remain vulnerable to noisy inputs [7]. Advanced attention-based models, which attain 94%-98% accuracy in binary informativeness tasks [8], face difficulties when applied to multiclass humanitarian categorization, especially for real-time scenarios demanding sub-second inference [9]. Two critical gaps persist in the current research. First, as noted in [10], crisis tweets often feature complex cross-modal relationships. For instance, a tweet discussing \"earth-quake rescue efforts\" might accompany flood imagery, creating semantic mismatches that existing models struggle to resolve [5]. Second, modality-driven biases are prevalent: text typically dominates datasets, leading models to underutilize valuable visual cues. In Crisis-MMD, there are 2666 non-humanitarian text samples compared to fewer than 1000 related to rescue efforts [2], a disparity that [11] identifies as a significant factor causing models to overlook visual information. Recent advancements in contrastive learning, exemplified by CLIP [7] and ALIGN [11], show promise in learning robust multimodal representations. [PERSON] et al. [1] demonstrate that fine-tuned CLIP models outperform traditional fusion methods by 1.55-2.33% on CrisisMMD, leveraging large-scale pre-training to handle noisy data. Similarly, [PERSON] et al. [3,8] propose a middle fusion paradigm that combines cross-modal and self-attention, achieving 91.53% accuracy in tweet informativeness classification and highlighting the effectiveness of intermediate feature refinement over early or late fusion strategies. Building on these insights, our framework, CLIP-BCA-Gated, introduces several key innovations:  
1.