Investigating toxicity changes of cross-community redditors from 2 billion posts and comments

Area of Science:

• Computational Social Science
• Natural Language Processing
• Machine Learning

Background:

• Understanding online user behavior and content toxicity is crucial for community health.
• Previous research has focused on toxicity detection but less on dynamic user behavior across communities.

Purpose of the Study:

• To investigate how user toxicity changes within and across Reddit communities over time.
• To develop and validate a machine learning model for predicting toxicity in user-generated content.
• To analyze the relationship between user behavior, community norms, and content toxicity.

Main Methods:

• A labeled dataset of 10,083 Reddit comments was created using crowdsourcing.
• A Bidirectional Encoder Representations from Transformers (BERT) model was trained and fine-tuned for toxicity classification.
• The BERT model analyzed toxicity levels in over 87 million posts and 2.2 billion comments from 2005 to 2020.
• Time series analysis, including the Granger causality test, was employed to examine the relationship between links and toxicity.

Main Results:

• The BERT model achieved 91.27% accuracy and an AUC of 0.963 in toxicity detection.
• 16.11% of users posted toxic content, and 13.28% commented toxically.
• A significant portion of users (30.68% for posts, 81.67% for comments) showed varying toxicity levels across communities.
• Toxic comments were found to be Granger caused by the volume of links in comments.

Conclusions:

• Users adapt their online toxicity behavior based on the norms of different Reddit communities.
• The BERT model provides a robust method for analyzing large-scale user-generated content toxicity.
• The presence of links in comments is a significant factor influencing the emergence of toxic comments.