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Generating insights about financial asks from Reddit posts and user interactions

Sachin Thukral, Suyash Sangwan, Vipul Chauhan, Arnab Chatterjee, Lipika Dey

TL;DR

This work proposes methods for content analysis that can generate human-interpretable insights using topic-centered clustering and multi-document abstractive summarization for a large repository created from social media content.

Abstract

As an increasingly large number of people turn to platforms like Reddit, YouTube, Twitter, Instagram, etc. for financial advice, generating insights about the content generated and interactions taking place within these platforms have become a key research question. This study proposes content and interaction analysis techniques for a large repository created from social media content, where people interactions are centered around financial information exchange. We propose methods for content analysis that can generate human-interpretable insights using topic-centered clustering and multi-document abstractive summarization. We share details of insights generated from our experiments with a large repository of data gathered from subreddit for personal finance. We have also explored the use of ChatGPT and Vicuna for generating responses to queries and compared them with human responses. The methods proposed in this work are generic and applicable to all large social media platforms.

Generating insights about financial asks from Reddit posts and user interactions

TL;DR

This work proposes methods for content analysis that can generate human-interpretable insights using topic-centered clustering and multi-document abstractive summarization for a large repository created from social media content.

Abstract

As an increasingly large number of people turn to platforms like Reddit, YouTube, Twitter, Instagram, etc. for financial advice, generating insights about the content generated and interactions taking place within these platforms have become a key research question. This study proposes content and interaction analysis techniques for a large repository created from social media content, where people interactions are centered around financial information exchange. We propose methods for content analysis that can generate human-interpretable insights using topic-centered clustering and multi-document abstractive summarization. We share details of insights generated from our experiments with a large repository of data gathered from subreddit for personal finance. We have also explored the use of ChatGPT and Vicuna for generating responses to queries and compared them with human responses. The methods proposed in this work are generic and applicable to all large social media platforms.
Paper Structure (8 sections, 3 equations, 3 figures, 2 tables)

This paper contains 8 sections, 3 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. A framework for analyzing large social platforms
  3. Content Sensing using Topical Clustering
  4. A hybrid text clustering algorithm
  5. Measuring Platform Engagement
  6. Experiments, Results and Observations
  7. Related Work
  8. Conclusion

Figures (3)

  • Figure 1: Engagement of Active users vs Passive users
  • Figure 2: Topic-wise percentage of Posts in two timelines
  • Figure 3: Topic-wise comparison of (Left) active and (Right) passive engagement in both the timelines