Generalizing to Unseen Disaster Events: A Causal View

The rapid growth of social media platforms has transformed disaster monitoring, yet biases in existing systems limit their effectiveness. The base article emphasizes the need for innovative approaches to mitigate these biases, aligning with recent studies on improving performance in various contexts, such as social bot detection and video anomaly detection. These related works also highlight the importance of robust methodologies in handling diverse scenarios, suggesting a broader trend towards enhancing machine learning applications in unpredictable environments. By integrating causal learning and debiasing techniques, the proposed method could significantly advance the field of disaster response, ensuring more reliable insights from social media data.