Deep Learning based Multi-Label Image Classification of Protest Activities

TL;DR

This paper employs deep learning to classify protest images from social media, enhancing dataset labeling and visualization to better understand societal unrest in urban areas.

Contribution

It introduces an improved protest image dataset and applies deep learning for multi-label classification and visualization of protest activities.

Findings

High accuracy in multi-attribute image prediction

Effective visualization of protest distribution across regions

Enhanced dataset quality for protest analysis

Abstract

With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of user-generated data can be used to enhance our understanding of significant societal issues particularly for urbanizing areas. In order to better analyze protest behavior, we enhanced the GSR dataset and manually labeled all the images. We used deep learning techniques to analyze social media data to detect social unrest through image classification, which performed good in predict multi-attributes, then also used map visualization to display protest behaviors across the country.