posted on Jun, 14 2013 @ 03:48 AM
reply to post by teachtaire
The most likely method that would be attempted is to set up a translation dictionary of at least nouns and verbs with perhaps qualifiers as to
degree. Examples might be words like: protest, anger, violent, peaceful, crowd, group, party, meeting, victim, authority)
At the first level, one could count the occurrence of the various nouns and verbs or noun-verb pairs in the News, Web pages, tweets, or any other
common source of interest. One could use the modifiers as a weight where it makes sense (eg few, thousands, many, etc). A deeper level, which would
probably be more productive, would be to establish "vectors" of factors which indicate mood, concern, lack of interest, and similar abstract
concepts. The associated data would be the political actions or lack thereof which occurred in a time window after the data window, say maybe a week
later. The vectors could be developed using this type of data plus the events.
As long as the underlying relationships and quantities hold, it would be possible to make predictions and probably include confidence bounds via
simulations. I'd probably try a weighted probabilistic decision tree or something like a random forest. Confidence bounds could be added through
There are limitations which are pretty serious: The underlying relationships which determine probability that individuals act out may be driven by
economic settings, current social issues,transportation availability or cost and so forth - causing a once predictive model to become non-predictive
as the situations change. Then again, once predictions become possible (even if tenuous) some will try to influence the results by altering the
content on the Web or News if they can afford to do so. This too would end up altering the predictability.
Hope this helps.