Towards the Monitoring of Violent Events in Social Media through Visual Information

Alejandro Escalante-Hernandez, Luis Joaquín-Arellano, Jose de Jesús Lavalle-Martínez, Luis Villaseñor-Pineda, Hugo Jair Escalante


Violence is a latent threat for individuals, this is an even more concerning issue in Latin American cities. The detection and monitoring of events reported in social media could help to build maps of zones with incident of violent events. This could eventually lead to the automatic generation of risk maps which could be of great help to users and even authorities. This paper aims to detect violent incidents reported in social media using visual information only. While most of the related work focuses on the text modality, the goal of this paper is to assess the feasibility of detection when only visual information is available. CNN based feature extraction and standard classification models are implemented and evaluated in a recently released corpus. Experimental results show that distinguishing images depicting violent events is feasible, but the fine grain recognition of categories is still an open problem.


Violent event detection, social media analysis, image classification with CNNs

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