# Deep Learning based Multi-Label Image Classification of Protest Activities

Source: [Deep Learning based Multi-Label Image Classification of Protest Activities](<https://www.sota2.com/research/paper/68fa6e571e209131cea3f2b8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:55:03.570Z

Authors: Yingzhou Lu, Kosaku Sato, Jialu Wang

Published: 2023-01-10T21:25:53.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.04212>)

[PDF](<https://arxiv.org/pdf/2301.04212.pdf>)