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Dynamics of online hate and misinformation
Matteo Cinelli1, Andraž Pelicon2,3, Igor Mozetič2
1Ca' Foscari University of Venice, Venice, Italy.
Scientific Reports
|November 12, 2021
Summary
Online debates show increasing toxicity. Machine learning analysis of YouTube comments reveals users from reliable sources use more toxic language, and discussions worsen over time.
Area of Science:
- Computational Social Science
- Natural Language Processing
- Online Behavior Analysis
Background:
- Online debates are increasingly polarized and hostile.
- Hate speech on digital platforms necessitates effective countermeasures.
- Understanding user behavior in online discussions is crucial.
Purpose of the Study:
- To detect hate speech in a large YouTube comments dataset.
- To analyze user behavior contributing to online toxicity.
- To investigate the relationship between source reliability and language toxicity.
Main Methods:
- Utilized a machine learning model for hate speech detection.
- Trained and fine-tuned the model on over one million hand-annotated YouTube comments.
- Analyzed user interactions and language patterns within online debates.
Main Results:
- No evidence of users exclusively posting hateful comments ('pure haters') was found.
- Users favoring specific channel types (questionable/reliable) exhibited more toxic language towards opposing communities.
- Loyal users of reliable sources displayed higher average toxicity than those of questionable sources.
- Discussion toxicity correlated positively with discussion length (comment count and time).
Conclusions:
- Online debates tend to escalate in toxicity, aligning with Godwin's Law.
- Echo chamber effects influence the use of hateful and violent language.
- Source credibility influences user toxicity levels in online discussions.
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