Author: Denis Avetisyan
A new deep learning framework leverages both images and text from social media to accurately identify disaster-related content in the Bangla language.
BanglaMM-Disaster achieves 83.76% accuracy in multiclass disaster classification by fusing textual and visual data with a transformer-based architecture.
Despite increasing efforts in disaster preparedness, rapid and accurate damage assessment remains a critical challenge, particularly in low-resource language contexts. This paper introduces BanglaMM-Disaster: A Multimodal Transformer-Based Deep Learning Framework for Multiclass Disaster Classification in Bangla, a novel approach to classifying disaster-related social media posts by effectively integrating textual and visual information. Achieving 83.76% accuracy, the framework significantly outperforms unimodal baselines, demonstrating the benefits of multimodal learning for timely disaster response. Could this approach pave the way for more robust and accessible early warning systems in Bangla-speaking regions and beyond?
The Inevitable Noise: Filtering Signals in the Wake of Disaster
The swift identification of disaster-related information shared on social media platforms is now paramount for timely and effective response efforts, yet this process is significantly challenged by the inherent complexities of natural language processing. Human communication, particularly in crisis situations, is rarely straightforward; ambiguity, slang, and contextual cues frequently obscure meaning, demanding sophisticated analytical tools to decipher the true nature of a post. Furthermore, the sheer volume of data generated during a disaster overwhelms conventional methods, requiring automated systems capable of filtering noise and prioritizing critical updates. Successfully navigating these linguistic hurdles is not merely a technical problem, but a fundamental necessity for minimizing harm and maximizing the impact of relief operations, as delays in understanding the situation on the ground can have devastating consequences.
Current automated systems designed to extract critical information from social media during disasters frequently falter when processing Bangla content. This difficulty stems not only from the inherent complexities of the Bangla language – its morphology, contextual ambiguities, and diverse dialects – but also from the way information is typically shared online. Social media posts are rarely solely textual; they often combine text with images, videos, and emojis, creating a multimodal challenge for algorithms trained primarily on text. Existing natural language processing techniques struggle to integrate and interpret these different data types effectively, leading to inaccuracies in disaster event detection and impact assessment. The subtle cues conveyed through visual content, often crucial for understanding the severity of a situation, are frequently overlooked, hindering the ability to rapidly and reliably identify those most in need of assistance.
The limited availability of comprehensive, labeled datasets specifically designed for Bangla disaster classification presents a significant obstacle to developing effective automated response systems. Unlike English, where substantial resources exist for training machine learning models to identify disaster-related content, Bangla lacks similarly scaled collections of annotated social media posts. This scarcity forces researchers to rely on smaller, often manually created datasets, or to adapt models trained on other languages – approaches that frequently compromise accuracy due to linguistic and cultural differences. Consequently, systems designed to filter and prioritize critical information during emergencies struggle to reliably distinguish genuine disaster reports from everyday communication, hindering timely and effective aid delivery. Building such a resource requires considerable effort, encompassing data collection, meticulous annotation, and ongoing maintenance to reflect the evolving language used in crisis situations.
BanglaMM-Disaster: A Framework for Deciphering the Signals
BanglaMM-Disaster employs a multimodal architecture combining transformer-based text encoders, specifically XLM-RoBERTa, with convolutional neural networks (CNNs) to extract relevant features from disaster-related data. XLM-RoBERTa processes textual information, generating contextualized word embeddings, while CNNs analyze images to identify visual patterns indicative of disaster events. This integration allows the framework to leverage the strengths of both modalities: transformers excel at understanding semantic relationships in text, and CNNs are effective at recognizing visual features. The resulting feature vectors from both encoders are then combined for downstream classification tasks, enhancing the overall robustness and accuracy of disaster event identification.
BanglaMM-Disaster addresses the challenges of processing the Bangla language through the implementation of WordPiece tokenization, a subword segmentation technique that effectively handles the language’s morphological complexity and large vocabulary. To further enhance data quality, the framework incorporates the Google Translate API, which is utilized for identifying and correcting inconsistencies or errors present in the original Bangla text data. This API-driven approach facilitates data cleaning and normalization, improving the reliability and accuracy of the input features used for disaster event classification.
Early fusion, as implemented in BanglaMM-Disaster, concatenates the feature vectors derived from both textual and visual data streams prior to passing them into subsequent layers of the deep learning model. This contrasts with later fusion techniques which process each modality independently before merging representations. By combining features at an early stage, the model facilitates direct interactions between textual and visual information, enabling it to learn cross-modal relationships and potentially capture synergistic effects not readily apparent when processing each modality in isolation. The concatenated vector is then processed by fully connected layers and classification layers to predict disaster event categories.
The BanglaMM-Disaster framework classifies disaster events by processing and integrating information from multiple data modalities, specifically text and images. This classification is achieved through a deep learning architecture designed to identify patterns and correlations within the combined data. The model doesn’t rely on single data sources; instead, it aims for a holistic understanding of the event as described in both textual reports and accompanying visual evidence. Output is categorized according to predefined disaster types, enabling automated event identification and potentially facilitating rapid response efforts. The system’s accuracy is predicated on its ability to effectively correlate features extracted from both modalities to determine the most likely disaster classification.
The BanglaMM-Disaster Dataset: Anchoring the System in Reality
The BanglaMM-Disaster Dataset consists of 5,037 Bangla-language social media posts collected from various online sources. These posts document a range of disaster events, including floods, cyclones, earthquakes, and landslides. Data curation involved identifying relevant posts and removing irrelevant content, while annotation focused on labeling each post with information pertaining to the type of disaster described. The dataset represents a focused collection of user-generated content intended to support research in natural language processing and disaster management within the context of the Bangla language.
The BanglaMM-Disaster dataset directly responds to the scarcity of labeled data in Bangla for natural language processing tasks. Effective disaster classification models, crucial for timely response and damage assessment, require substantial training data with accurate annotations. Currently available resources are limited, hindering the development of robust systems capable of automatically identifying disaster-related content in Bangla social media. This dataset provides the necessary labeled instances to facilitate supervised learning approaches, enabling researchers to train, validate, and benchmark disaster classification algorithms specifically tailored for the Bangla language. The availability of this resource will contribute to improved accuracy and reliability in automated disaster information processing systems.
Inter-annotator agreement for the BanglaMM-Disaster dataset was quantified using Cohen’s Kappa, a statistical measure of agreement for qualitative items. The resulting Kappa score of 0.82 indicates a high level of agreement between annotators, falling within the range generally considered to represent “almost perfect” agreement. This rigorous assessment confirms the reliability and consistency of the annotations within the dataset, bolstering confidence in the quality of the labeled data and its suitability for training and evaluating machine learning models designed for disaster-related text classification in Bangla.
The BanglaMM-Disaster Dataset is intended to facilitate research and development in Bangla natural language processing, specifically within the domain of disaster management. Containing 5,037 annotated social media posts, the dataset provides a benchmark for evaluating and comparing disaster-related classification models. Researchers can utilize this resource to train machine learning algorithms for tasks such as identifying disaster types, assessing event severity, and extracting critical information from unstructured text. The availability of a labeled Bangla dataset addresses a significant gap in resources for low-resource language NLP and promotes innovation in disaster information processing for the Bengali-speaking population.
A System Tested, and a Path Forward
BanglaMM-Disaster demonstrably advances the field of disaster classification for Bangla content, achieving state-of-the-art accuracy of 83.76%. This performance represents a significant leap forward, exceeding the capabilities of models relying solely on textual data by 3.84%. More strikingly, the framework surpasses the performance of models based exclusively on image analysis by a substantial 16.91%. These results highlight the power of multimodal learning, effectively combining visual and textual cues to achieve a more comprehensive and accurate understanding of disaster-related information within the Bangla language. The enhanced accuracy promises improved disaster response and resource allocation in affected regions.
The BanglaMM-Disaster framework leverages the power of Convolutional Neural Networks (CNNs) initially trained on the extensive ImageNet dataset to reliably extract meaningful visual features from disaster-related imagery. This pre-training is crucial, allowing the model to generalize effectively even with limited disaster-specific image data. Complementing this visual processing, the training process itself is optimized through the use of the Adam optimizer, an adaptive learning rate algorithm that accelerates convergence, and Categorical Cross-Entropy Loss, a function that effectively measures the difference between predicted and actual disaster classifications. This combination of robust feature extraction and optimized training ensures the model learns efficiently and achieves high accuracy in identifying disaster types from multimodal data.
BanglaMM-Disaster demonstrates practical applicability through its efficient operational characteristics. Measurements reveal an inference time of just 0.45 seconds, enabling near real-time disaster classification, while the memory footprint remains manageable at 1.8 GB when utilizing standard GPU hardware. These metrics suggest the framework is poised for deployment on readily available infrastructure, facilitating its integration into existing disaster monitoring and response workflows. The combination of speed and relatively low resource demand positions BanglaMM-Disaster as a viable solution for rapid assessment and effective resource allocation during critical events.
Continued development of BanglaMM-Disaster prioritizes several key advancements to enhance its utility and impact. Researchers intend to significantly expand the training dataset, incorporating a wider range of disaster events and geographic locations to improve generalization and robustness. Simultaneously, investigation into more sophisticated multimodal fusion techniques aims to move beyond simple concatenation of features, potentially leveraging attention mechanisms or transformer networks to better integrate textual and visual information. Ultimately, the goal is to transition from a research prototype to a deployed system, integrating BanglaMM-Disaster into real-world disaster monitoring platforms to provide timely and accurate assessments of damage and facilitate more effective response efforts.
The pursuit of accuracy in disaster classification, as demonstrated by BanglaMM-Disaster, inevitably confronts the decay inherent in all systems. The framework’s 83.76% accuracy is not a final state, but a momentary equilibrium against the inevitable erosion of model performance as data distributions shift. This work, through its multimodal fusion of text and image data, attempts to build a resilient system, acknowledging that every failure is a signal from time. As Henri Poincaré observed, “Mathematics is the art of giving reasons.” Similarly, this framework offers a reasoned approach to a complex problem, yet remains subject to the relentless pressure of temporal change. Refactoring, in this context, isn’t merely about improving code, but a dialogue with the past – a continuous recalibration against the decay of information.
What Lies Ahead?
The reported 83.76% accuracy, while a demonstrable advance, merely establishes a new baseline for decay. Any improvement in disaster classification, particularly one reliant on the volatile currents of social media, ages faster than expected. The inherent ambiguity of language, and the ever-shifting visual vernacular, will inevitably erode performance. The true challenge isn’t achieving a higher score today, but minimizing the rate of decline tomorrow.
Current approaches, including this framework, largely treat data as a static artifact. However, disaster contexts are dynamic; the semantics of ‘disaster’ itself are not fixed. Future work must grapple with temporal drift – the evolution of language and imagery during a crisis. A system trained on past events will, by necessity, become less relevant as new patterns emerge. Rollback-a journey back along the arrow of time to recalibrate the model with fresh data-will become increasingly critical, and increasingly difficult.
The fusion of modalities, while effective, masks a deeper problem. The framework, like many others, implicitly assumes a stable relationship between text and image. But the meaning of a photograph, its emotional resonance, is profoundly context-dependent. True progress requires not simply combining information, but modeling the complex, evolving interplay between visual and textual signals, acknowledging that even the most robust system is ultimately subject to the entropic forces governing all complex systems.
Original article: https://arxiv.org/pdf/2511.21364.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-11-28 01:37