Title: Analyzing and Developing Indicators for Building an Automatic Detector of Fake News in Social Media
Supervisor: Stan Matwin, Dalhousie University; Co-supervisor: Luis Torgo
PhD in Computer Science, Dalhousie University
Summary
An enormous number of mobility datasets for tracking animals, vehicles, vessels, individuals and moving objects are currently available, and this number continues to grow. Mobility data has a diversity of applications including transportation, marine navigation, tourism, animal behavior analysis, environmental science. Since the rate of generation of the mobility data is exceptionally high, processing such data demands some reasonable preprocessing and cleaning steps. Segmenting the traces of move- ments in mobility data is an essential task for preprocessing, which is called trajectory segmentation. The significance of this preprocessing step has three dimensions. First, it is crucial to data sammarization, so that it can alleviate the curse of dimensionality. Second, correctly using a segmentation method can contribute to increase the level of preserving the privacy of mobility data. Third, it can be employed in embedded systems to diminish energy consumption and make mobility sensor devices operating for a longer time.
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