Conclusion

This project set out to explore the intricate relationship between emotions and post resonance on Reddit, focusing on two emotion-rich subreddits: r/TIFU and r/confession. The goal was to understand what makes certain posts more engaging and explore how emotional content influences popularity and user agreement.

Exploratory Insights

We began by addressing foundational questions to understand the dynamics of these subreddits:

  • Key Contributors and Themes: Identifying the most active users and recurring discussion topics provided a lens into the heartbeat of these communities. Stories often revolved around personal experiences, confessions, and emotions.
  • Emotions in Titles and Content: Posts with emotional depth, particularly in their titles, drew more attention. Emotions like anger, anticipation, and trust dominated these subreddits, with anger being especially prevalent in titles.
  • Role of Controversy and Comments: Posts with controversial tones were found to spark more engagement, and the volume of comments often correlated with higher scores. However, this engagement was not necessarily tied to resonance but could stem from polarizing discussions.

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Diving into Sentiments

Our deeper sentiment analysis uncovered more specific trends:

  • Emotion Distributions: While anger dominated titles, emotions like anticipation, trust, and joy featured prominently in post content. This discrepancy between titles and content emotions suggested a strategic use of titles to capture attention.
  • Sentiment and Upvotes: Posts with positive sentiment garnered the highest average upvotes, followed by neutral sentiment. Negative sentiment, while prevalent, was less likely to receive high upvotes, emphasizing the community’s preference for uplifting or relatable content.
  • Post-Comment Sentiment Dynamics: Submissions with positive sentiments elicited more positively aligned comments, while negative submissions attracted mixed or polarizing reactions, highlighting the complexity of audience engagement.

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Machine Learning: Popularity and Agreement

The machine learning component of the project aimed to quantify these insights.

  1. Popularity Prediction: Using sentiment scores and post metadata, we built models to predict post popularity. Gradient Boosting achieved the best performance, but feature importance revealed that emotional features, while significant, were not the dominant predictors. Key features included post length and emotional tones like anticipation and regret.

  2. Agreement Prediction: Shifting focus from popularity to resonance, we incorporated comment data to predict user agreement with posts. Random Forest outperformed other models, demonstrating that emotions like shame, regret, and joy were strong drivers of agreement. Notably, the score column used for popularity showed little importance, underscoring the difference between popularity and resonance.

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Future Work and Reflections

This project revealed significant insights into how emotions shape engagement on Reddit. However, it also raised important questions:

  • Reddit’s Current Promotion System: The platform prioritizes upvotes to rank posts, favoring popularity over resonance. We propose incorporating an agreement score to highlight posts that deeply resonate with users, fostering a more supportive and meaningful community.
  • The Role of Controversy: Professor Arora suggested exploring the inverse relationship between controversy and popularity. Posts with higher disagreement levels might achieve greater popularity, highlighting the business value of polarizing content. Future research could investigate this “controversy effect” and its implications for user engagement.

In conclusion, while this project focused on resonance, it also acknowledged the dual role of Reddit as both a community-building platform and a business. Balancing resonance and controversy offers a pathway for platforms to engage users meaningfully while maintaining active participation. Future iterations of this work could explore these trade-offs further, broadening our understanding of online community dynamics.