Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
This addresses cyberbullying detection for social media users by overcoming platform, topic, and feature bottlenecks, though it is incremental in applying deep learning to this domain.
The paper tackled the problem of cyberbullying detection across multiple social media platforms and topics by using deep learning models, achieving transfer learning across datasets including Formspring (12k posts), Twitter (16k posts), and Wikipedia (100k posts).
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.