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.

Petra Kralj Novak, Peter Gabrovsek and Igor Mozetic
Tuesday, September 25, 2018 - 11:00 to 11:15


The official Hotel of the Conference is
Makedonia Palace.

Conference Organiser: NBEvents

The official travel agency of the Conference is: Air Maritime

Photo of Thessaloniki seafront courtesy of Juli Bellou
fb flickr flickr