Cyberbullying or just Sarcasm? Unmasking Coordinated Networks on Reddit

TL;DR

This paper develops an NLP and machine learning framework to distinguish cyberbullying from sarcasm on Reddit, achieving high accuracy and revealing vulnerable groups and coordinated harmful networks.

Contribution

It introduces a novel approach combining NLP and machine learning to differentiate cyberbullying from sarcasm, addressing limitations of traditional sentiment analysis.

Findings

Achieved 95.15% accuracy in detection

Identified vulnerable groups such as teenagers and minorities

Uncovered coordinated networks involved in cyberbullying

Abstract

With the rapid growth of social media usage, a common trend has emerged where users often make sarcastic comments on posts. While sarcasm can sometimes be harmless, it can blur the line with cyberbullying, especially when used in negative or harmful contexts. This growing issue has been exacerbated by the anonymity and vast reach of the internet, making cyberbullying a significant concern on platforms like Reddit. Our research focuses on distinguishing cyberbullying from sarcasm, particularly where online language nuances make it difficult to discern harmful intent. This study proposes a framework using natural language processing (NLP) and machine learning to differentiate between the two, addressing the limitations of traditional sentiment analysis in detecting nuanced behaviors. By analyzing a custom dataset scraped from Reddit, we achieved a 95.15% accuracy in distinguishing harmful content from sarcasm. Our findings also reveal that teenagers and minority groups are particularly vulnerable to cyberbullying. Additionally, our research uncovers coordinated graphs of groups involved in cyberbullying, identifying common patterns in their behavior. This research contributes to improving detection capabilities for safer online communities.