Effective Disaster Management Through Transformer-Based Multimodal Tweet Classification

Abstract

The role of social media in crisis response and recovery is becoming increasingly prominent due to the rapid progression of information and communication technologies. This study introduces a transformative approach to extract valuable information from the enormous volume of user-generated content on social media, specifically focusing on tweets that can significantly aid emergency response and recovery efforts. The identification of informative tweets allows emergency personnel to gain a more comprehensive understanding of crisis situations, thereby facilitating the deployment of more effective recovery strategies. Previous studies have largely focused on either the textual content or the accompanying visual elements within tweets. However, evidence suggests a complementary relationship between text and visuals, offering an opportunity for synergistic insights. In response to this, a novel deep learning framework is proposed, which concurrently analyses both textual and visual components extracted from user-generated tweets. The central architecture integrates established methodologies, including RoBERTa for text analysis, Vision Transformer for image understanding, Bi-LSTM for sequence processing, and an attention mechanism for context awareness. The innovation of this approach lies in its emphasis on multimodal fusion, introducing rank fusion techniques to effectively combine the strengths of textual and visual inputs. The proposed methodology is extensively tested across seven diverse datasets, representing various natural disasters such as wildfires, hurricanes, earthquakes, and floods. The experimental results demonstrate a superior performance of the proposed system, compared to several existing methods, with accuracy levels ranging from 94% to 98%. These findings underscore the efficacy of the proposed deep learning classifier in leveraging interactions across multiple modalities. In summary, this study contributes to disaster management by promoting a comprehensive approach that exploits the potential of multimodal data, thereby enhancing decision-making processes in emergency scenarios.

LAMBDA: Lexicon and Aspect-Based Multimodal Data Analysis of Tweet

“…The writes of [17] faces difficulties with low Precision, F1 Score, Recall, and relatively high False Positives. Lastly, An Approach [26] showcases robust performance with balanced Precision, F1 Score, Recall, and a solid Accuracy score. In summary, the LAMBDA Approach emerges as a strong contender, excelling in multiple performance metrics compared to the referenced approaches, signifying its effectiveness in sentiment analysis.…”

“…For Text-based sentiment analysis, various approaches have been explored. Approach [2] achieved an accuracy of 82.90%, while [26] achieved a higher accuracy of 85.40%. However, Poria et al [15] lagged behind with an accuracy of 77.30%, and Jurek et al [17] demonstrated an accuracy of 84.62%.…”

“…P3-Tokenize T3 P4-Remove stop words from T3 P5-Perform POS tagging on T3 P6-Apply stemming and lemmatization to T3 (25). P = Process the features of {P3, P4, P5, P6} (26). K3 = Extract aspect keywords from P3 (27).…”

“…These evaluation metrics help assess the performance of lexicon-based sentiment analysis approaches in terms of accuracy, precision, recall, and discrimination ability, providing insights into the model's effectiveness in predicting sentiment polarity. The Table 3 presents a comprehensive analysis of the performance metrics for various sentiment analysis approaches, including the LAMBDA Approach and several referenced methods labelled as [1,2,15,17], and [26]. These metrics encompass True Positives (TP), True Negatives (TN), False Positives (FP), False Negatives (FN), Precision, F1 Score, Recall, and Accuracy, which collectively evaluate the efficacy of these approaches.…”

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

“…BERT-based (e.g., RoBERTa, DistilBERT) [23][24][25]29,32,38,39] Superior performance in various tasks, including textual and visual analysis.…”