Spam, Scam & Ham in Twitter Forex trading signals

Currency trading (Forex) is the largest world market in terms of volume. We analyzed trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model was constructed. We used the model to classify all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD). The Twitter stance was compared to the actual currency rates by applying the event study methodology, well-known in financial economics. It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users that we have identified: trading robots, spammers, trading companies, and individual traders.
Additionally, we have observed attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline.