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This research aims to predict viral content, analyze sentiment evolution, model topics, and track comment lifecycles over time. To achieve these objectives, machine learning techniques, including Random Forest, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Natural Language Processing models, are employed. Random Forest achieved 95.27% accuracy in predicting content virality, while SARIMA effectively modeled engagement trends. Sentiment analysis using VADER revealed fluctuations in users’ emotions. Bias detection highlighted gender-related disparities in discussions. Topic modeling with BERTopic uncovered dominant themes, providing insights into evolving online discourse. These findings contribute to real-time social media analytics, aiding in trend forecasting and bias detection.

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Sentiment, Bias, and Virality: Investigating Content Dynamics on Reddit

  • 会议论文

  • 发表日期：

    2026年

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  • V. Aswin,
  • N. Lalithamani

摘要

This research aims to predict viral content, analyze sentiment evolution, model topics, and track comment lifecycles over time. To achieve these objectives, machine learning techniques, including Random Forest, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Natural Language Processing models, are employed. Random Forest achieved 95.27% accuracy in predicting content virality, while SARIMA effectively modeled engagement trends. Sentiment analysis using VADER revealed fluctuations in users’ emotions. Bias detection highlighted gender-related disparities in discussions. Topic modeling with BERTopic uncovered dominant themes, providing insights into evolving online discourse. These findings contribute to real-time social media analytics, aiding in trend forecasting and bias detection.