Stock trading and the like have always been at the forefront of data-mining — though not often sharing their techniques, for obvious reasons.
The current trendy data-mining topic* is sentiment analysis based on social media — guessing what the world thinks about a topic by searching for positive or negative opinions about it on twitter &c. Roughly, searching for “I love X” versus “I hate X”, and interpreting that as a sign of general opinion.
There are surely traders basing decisions on sentiment analysis. It’s anybody’s guess how many, or how seriously, but it’s going to grow over time.
So when is the spam coming?
Go short on company X. Spam twitter with ‘X sucks’ messages. Wait for other traders to use sentiment analysis, see X is unpopular, and dump their shares. Buy cheap. Profit.
You maybe couldn’t affect a major company like this — the market isn’t *that* stupid. But suppose you know another trader is using sentiment analysis, and have a hunch that you can make her buy or sell by dumping enough positive or negative opinions online? Isn’t that a strong incentive to spam?
[this inspired by a post suggesting that you predict layoffs by seeing whose employees are updating their CVs on linkedin — an idea so sensible that it’s probably already being used by a dozen companies]
* or rather, trendy among in the world of starry-eyed startups — there’s somewhat less academic interest. Probably because it produces results which are (a) easy to interpret, and (b) utterly unreliable.