Random Forest location prediction from social networks during disaster events.
Abstract
Rapid location and classification of data posted on
social networks during time-critical situations such as natural
disasters, crowd movement and terrorism is very useful way
to gain situational awareness and to plan response efforts.
Twitter as successful real time micro-blogging social media, is
increasingly used to improve resilience during extreme weather
events/emergency management situations, including earthquake.
It being used during crises by communicating potential risks
and their impacts by informing agencies and officials. The
geographical location information of such events are vital to
rescue people in danger, or need assistance. However, only few
messages contains there native geographical coordinates (GPS).
So identifying location is a real challenge with Twitter data during
critical situations. Identification of Tweets and their precise
location are still inaccurate. In this work, we propose to use
semi-supervised technique to utilize unlabeled data, which is often
abundant at the onset of a crisis event, along with fewer labeled
data. Specifically, we adopt an iterative Random Forest fittingprediction
framework to learn the semi-supervised model.
Origin : Files produced by the author(s)
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