Protest Activity Detection and Perceived Violence Estimation from Social Media Images

TL;DR

This paper introduces a multi-task neural network model that detects protesters, describes their activities, and estimates perceived violence from social media images, supported by a new annotated protest image dataset.

Contribution

The study presents a novel multi-task CNN model and releases the UCLA Protest Image Dataset for analyzing protest activities and violence levels from social media images.

Findings

Model accurately classifies protesters and visual attributes.

Effective estimation of perceived violence and emotions.

Dataset enables comprehensive protest image analysis.

Abstract

We develop a novel visual model which can recognize protesters, describe their activities by visual attributes and estimate the level of perceived violence in an image. Studies of social media and protests use natural language processing to track how individuals use hashtags and links, often with a focus on those items' diffusion. These approaches, however, may not be effective in fully characterizing actual real-world protests (e.g., violent or peaceful) or estimating the demographics of participants (e.g., age, gender, and race) and their emotions. Our system characterizes protests along these dimensions. We have collected geotagged tweets and their images from 2013-2017 and analyzed multiple major protest events in that period. A multi-task convolutional neural network is employed in order to automatically classify the presence of protesters in an image and predict its visual attributes, perceived violence and exhibited emotions. We also release the UCLA Protest Image Dataset, our novel dataset of 40,764 images (11,659 protest images and hard negatives) with various annotations of visual attributes and sentiments. Using this dataset, we train our model and demonstrate its effectiveness. We also present experimental results from various analysis on geotagged image data in several prevalent protest events. Our dataset will be made accessible at https://www.sscnet.ucla.edu/comm/jjoo/mm-protest/.