Prediction of Cyberbullying Incidents on the Instagram Social Network

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 and predict incidents of cyberbullying in Instagram, a media-based mobile social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, profanity, social graph features, temporal commenting behavior, linguistic content, and image content. Using the labeled data, we further design and evaluate the performance of classifiers to automatically detect and pre- dict incidents of cyberbullying and cyberaggression.

arXiv admin note: text overlap with arXiv:1503.03909

References in corpus (1)

• A Comparison of Common Users across Instagram and Ask.fm to Better Understand Cyberbullying

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• Cyberbullying Identification Using Participant-Vocabulary Consistency
• Detecting Online Hate Speech Using Context Aware Models
• A Survey on Multimodal Disinformation Detection
• Sentiment Analysis of Fashion Related Posts in Social Media
• Investigating Factors Influencing the Latency of Cyberbullying Detection
• Multimodal Learning for Hateful Memes Detection
• Creating a Multimodal Dataset of Images and Text to Study Abusive Language
• An Information Retrieval Approach to Building Datasets for Hate Speech Detection