Detection of Cyberbullying Incidents on the Instagram Social Network

TL;DR

This paper presents new methods for detecting cyberbullying on Instagram by analyzing images and comments, involving data collection, human labeling, and classifier development to automatically identify cyberbullying incidents.

Contribution

It introduces a novel approach combining image and comment analysis with machine learning to detect cyberbullying on Instagram.

Findings

Labeled dataset of Instagram images and comments for cyberbullying detection

Correlation analysis between features and cyberbullying incidents

A classifier achieving promising accuracy in automatic detection

Abstract

Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect incidents of cyberbullying over images in Instagram, a media-based mobile social network. To this end, we have collected a sample Instagram data set consisting of images and their associated comments, and designed a labeling study for cyberbullying as well as image content using human labelers at the crowd-sourced Crowdflower Web site. An analysis of the labeled data is then presented, including a study of correlations between different features and cyberbullying as well as cyberaggression. Using the labeled data, we further design and evaluate the accuracy of a classifier to automatically detect incidents of cyberbullying.