  For decades, the world of financial advisors has been dominated by large
investment banks such as Goldman Sachs. In recent years, user-contributed
investment services such as SeekingAlpha and StockTwits have grown to millions
of users. In this paper, we seek to understand the quality and impact of
content on social investment platforms, by empirically analyzing complete
datasets of SeekingAlpha articles (9 years) and StockTwits messages (4 years).
We develop sentiment analysis tools and correlate contributed content to the
historical performance of relevant stocks. While SeekingAlpha articles and
StockTwits messages provide minimal correlation to stock performance in
aggregate, a subset of authors contribute more valuable (predictive) content.
We show that these authors can be identified via both empirical methods or by
user interactions, and investments using their analysis significantly
outperform broader markets. Finally, we conduct a user survey that sheds light
on users views of SeekingAlpha content and stock manipulation.
