# Generating insights about financial asks from Reddit posts and user interactions

arXiv: https://arxiv.org/abs/2403.04308 · HTML: https://mlsanity.com/paper/2403.04308/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification](https://mlsanity.com/paper/2507.22932/) (2025)
- [Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged](https://mlsanity.com/paper/2609.20989/) (2026)

## Abstract (arXiv)

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.