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European Geosciences Union

Stephen Camilleri

Stephen Camilleri
Stephen Camilleri

ESSI Earth and Space Science Informatics

The 2019 Outstanding Student Poster and PICO (OSPP) Awards is awarded to Stephen Camilleri for the poster/PICO entitled:

Investigating the relationship between earthquakes and online news (Camilleri, S.; Azzopardi, J.; Agius, M. R.)

Click here to download the poster/PICO file.

Stephen Camilleri is a Masters graduate from the University of Malta, tutored by Dr Joel Azzopardi and co-tutored by Dr Matthew R. Agius. His research made use of text mining tools to automatically map in near real-time earthquake events determined from multilingual news articles published by 23 leading news agencies. His work, which is published in an IEEE AIKE paper entitled “Investigating the relationship between earthquakes and online news”, complements the awarded poster at EGU 2019. The poster showcased the complexities involved to retrieve and parse unstructured text (online news articles); to extract and cross-validate extracted information from the news content and; to map the online news with earthquake events extracted from the USGS earthquake catalogue. It also provided an explanation on the tests carried out to validate the method, interesting observations made from this study, and a brief on the graphic user interface of the application. This research paves the way for more in-depth analysis of the spatial and temporal correlation between media reporting and the nature of the event and has potentially important applications in other fields of interest, besides earthquakes including wildfires, floods and tsunamis.
The work builds on the list of past achievements, including the University of London (Goldsmiths) Degree in Computing and Information Systems, where Stephen was awarded First Class Honours. His final dissertation entitled “Equity recommendation technique using fuzzy logic control” included an application, which made use of genetic programming, fuzzy logic control and sentiment analysis to establish the most promising stocks available on the market.