ABSTRACT
This chapter is concerned with the development of a new information search system in social network platforms for analyzing events in case of natural and man-made disasters. In todays, the world has witnessed the role of many social networks such as Facebook, Flicker, Twitter, and YouTube for extracting key and crucial information when worldwide scale major events had occurred. A social network can also be considered as information link creator between users and available resources. This role has been boosted in tremendous amount during and after the recent disasters around the world; for example, the 2010 Philippine typhoon, the 2010 Haiti earthquake, the 2011 Brazil flood, and the 2011 Japan earthquake and tsunami and Boston Marathon Explosion 2013. In such emergency situations, extracting and analyzing key information from the congested information resources posted or hosted in variety of forms and norms through almost all social network platforms are of major concerns in assessing the situation and in decision making. Therefore, some mathematical modeling techniques of branching processes and Markov chain theory to investigate how new knowledge and new information about the disasters spreads on the social networks and how to extract trust and reliable key and dominant information are discussed here. Specifically, a set of text messages and visual information is transformed into a Markov chain to produce stationary and time dependent distributions. Then, the abnormalities and suspicious patterns occurring in the distributions are analyzed for detecting and extracting key information. Finally, some simulation results are given in the case of Boston Marathon Explosion occurred in April 15, 2013 by using three social networks Flickr, YouTube and Twitter information.
