Deep Learning based Multi-Label Image Classification of Protest Activities
This work addresses the need for better understanding of social unrest in urbanizing areas through image analysis, but it is incremental as it builds on existing datasets and methods.
The researchers tackled the problem of analyzing protest behavior by enhancing the GSR dataset with manual labeling and using deep learning for multi-label image classification from social media data, achieving good performance in predicting multiple attributes and visualizing protest behaviors across a country.
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.