Fusing Social Network Data with Hard Data


  1. Taghavi, E.
  2. Tharmarasa, R.
  3. Kirubarajan T.
  4. Boury-Brisset, A.C.
Corporate Authors
Defence Research and Development Canada, Valcartier Research Centre, Quebec QC (CAN);McMaster Univ, Hamilton ONT (CAN)
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 efficient mathematical techniques like Dempster–Shafer theory and Modified 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 Identification System (AIS), we can improve our event prediction capability. In this paper, we present some algorithms to fuse hard sensor data with soft social network data (tweets) in an effective manner. Preliminary results using real AIS data are also presented.
information fusion;Demspter-Shafer belief theory;airborne surveillance;event prediction;social data analysis
Report Number
DRDC-RDDC-2015-P082 — External Literature
Date of publication
02 Oct 2015
Number of Pages
Electronic Document(PDF)

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