Social networking sites such as Twitter, Facebook and Flickr play an important role
in disseminating breaking news about natural disasters, terrorist attacks and other
events. They serve as sources of first-hand information to deliver instantaneous news
to the masses, since millions of users visit these sites to post and read news items regularly.
Hence, by exploring e fficient mathematical techniques like Dempster-Shafer
theory and Modi ed Dempster's rule of combination, we can process large amounts of
data from these sites to extract useful information in a timely manner. In surveillance
related applications, the objective of processing voluminous social network data is to
predict events like revolutions and terrorist attacks before they unfold. By fusing the
soft and often unreliable data from these sites with hard and more reliable data from
sensors like radar and the Automatic Identi cation System (AIS), we can improve
our event prediction capability. In this paper, we present a class of algorithms to
fuse hard sensor data with soft social network data (tweets) in an e ffective manner.
Preliminary results using are also presented. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18382 |
| Date | 11 1900 |
| Creators | Thirumalaisamy, Abirami |
| Contributors | Kirubarajan, Thia, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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