  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.
