Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises

TL;DR

This paper introduces a real-time image filtering pipeline using deep learning and perceptual hashing to de-duplicate and filter social media images during crises, aiding emergency response efforts.

Contribution

It presents a novel image processing pipeline combining de-duplication and relevancy filtering for social media crisis imagery, improving data utility and resource efficiency.

Findings

Effective real-time filtering of crisis-related social media images

Significant reduction of redundant and irrelevant images

Enhanced resource utilization during emergencies

Abstract

The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.