Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms

TL;DR

This paper demonstrates that deep learning models can effectively detect cyberbullying across multiple social media platforms and topics, overcoming limitations of prior methods that were platform-specific and reliant on handcrafted features.

Contribution

It introduces a transfer learning approach that generalizes cyberbullying detection across different social media platforms and topics, a novel contribution in this domain.

Findings

Deep learning models transfer well across platforms.

Models outperform traditional handcrafted feature methods.

First systematic analysis of cross-platform, multi-topic cyberbullying detection.

Abstract

Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring (12k posts), Twitter (16k posts), and Wikipedia(100k posts). Our experiments provide several useful insights about cyberbullying detection. To the best of our knowledge, this is the first work that systematically analyzes cyberbullying detection on various topics across multiple SMPs using deep learning based models and transfer learning.