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Summary
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Analyzing online social media, this study introduces novel features to understand user reactions to posts. These discourse patterns reveal content genre and subtle semantic differences without analyzing discussion text.

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Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Information Dissemination

Background:

  • Online social media platforms foster diverse interactions and discussions across numerous subjects.
  • Understanding how user conversations reflect the original post's intent is crucial due to inherent user biases.

Purpose of the Study:

  • To develop and validate a novel set of features that capture discourse patterns in social media conversations.
  • To empirically investigate the relationship between a social media post's topic and the resulting user interactions.
  • To establish a new paradigm for analyzing information dissemination based on user reactions, bypassing direct text analysis of discussions.

Main Methods:

  • Introduction of unique features designed to capture microscopic trends in discourse patterns.
  • Application of these features to analyze macroscopic phenomena in online conversations.
  • Utilizing a dataset from Reddit to train and test classification models.

Main Results:

  • The developed features enable accurate classification of content genres.
  • The features can identify subtle semantic variations within a single topic.
  • Outlier content with substantially different subject matter can be effectively isolated.

Conclusions:

  • Novel discourse features provide an effective method for analyzing social media content and user engagement.
  • This approach offers a scalable way to understand information dissemination by focusing on reaction patterns rather than content analysis.
  • The findings have implications for content moderation, trend analysis, and understanding online discourse dynamics.