Paper Info
Title
Protest Activity Detection and Perceived Violence Estimation from Social Media Images.
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/.
Year
DOI
Venue
2017
10.1145/3123266.3123282
MM '17: ACM Multimedia Conference Mountain View California USA October, 2017
Keywords
DocType
Volume
Protest, Action and Activity Recognition, Scene Understanding, Social Media Analysis, Visual Sentiment Analysis
Journal
abs/1709.06204
ISBN
Citations 
PageRank 
978-1-4503-4906-2
2
0.37
References 
Authors
23
3
Name
Order
Citations
PageRank
Donghyeon Won140.74
Zachary C. Steinert-Threlkeld220.37
Jungseock Joo3524.61