Difference between revisions of "Controversial events detection"
m |
m |
||
| Line 9: | Line 9: | ||
Controversy-wise, Christmas day is relatively one sided, with most of the text mentioning it being relatively homogeneous. | Controversy-wise, Christmas day is relatively one sided, with most of the text mentioning it being relatively homogeneous. | ||
In contrast, the Presidential debates event will have obvious sides (supporting the different candidates). | In contrast, the Presidential debates event will have obvious sides (supporting the different candidates). | ||
| − | |||
Our goal is not only to detect controversial events, but also to discover what the different sides are - both grouping the individuals associated with each faction and describing how each faction talks about the event differently. | Our goal is not only to detect controversial events, but also to discover what the different sides are - both grouping the individuals associated with each faction and describing how each faction talks about the event differently. | ||
| + | We can use a probabilistic model to model these latent structures from the data without labeled training data. | ||
== Data == | == Data == | ||
Revision as of 20:12, 1 October 2012
Contents
Controversial event detection
Team members
Project idea
In our project, we propose to jointly detect events and the controversy surrounding it in the context of social media. For example, Christmas day is an event that receives the most attention around December 25th, while the Presidential debates once every four years. Controversy-wise, Christmas day is relatively one sided, with most of the text mentioning it being relatively homogeneous. In contrast, the Presidential debates event will have obvious sides (supporting the different candidates).
Our goal is not only to detect controversial events, but also to discover what the different sides are - both grouping the individuals associated with each faction and describing how each faction talks about the event differently.
We can use a probabilistic model to model these latent structures from the data without labeled training data.
