Recently, I stumbled across the abstract for a 2010 paper called “Thailand — Tourism and Conflict: Modeling Sentiment from Twitter Tweets Using Na├пve Bayes and Unsupervised Artificial Neural Nets. The purpose of the research was to:

…mine over 80 million twitter micro logs in order to explore whether data from this social media initiative can be used to identify sentiment about tourism and Thailand amid the unrest in that country during the early part of 2010 and further whether analysis of tweets can be used to discern the effect of that unrest on Phuket’s tourism environment.

It strikes me that mining such a quantity of data could provide all kinds of insights about all manner of important issues in mainland Southeast Asia. Not yet a social scientific revolution but, surely, a harbinger of things to come.

Another potential application of such analysis would be to explore word use/choice over time. Combined with tools like these, I could see how up-to-the-minute (but historically grounded) research on these topics would now be possible. Even 5 years ago this would have been a pipe-dream.