Analysis of Online News Coverage on Earthquakes Through Text Mining - Camilleri, Stephen; Agius, Matthew R.; Azzopardi, Joel (Frontiers in Earth Science, 2020)
News agencies work around the clock to report critical news such as earthquakes. We investigate the relationship between online news articles and seismic events that happen around the world. We have utilised computer text mining tools to automatically harvest, identify, cluster and extract information from earthquake-related reports and carry out cross-validation on the mined information. Earthquake parameters retrieved from the United States Geological Survey (USGS) Application Programming Interface (API) are organised into earthquake events, with each event consisting of daily earthquake readings taking place in a particular geographical location. The results are then visualised on a user-friendly dashboard. 268,182 news reports published by 23 news agencies from different parts of the world and 14,677 earthquakes of magnitude ranging from 4 to 8.2 listed in the bulletin were processed during a 1-year study between 2018 and 2019 resulting in 732 news clusters. The use of multilingual news sources from across six continents gives the advantage of reducing potential news bias originating from English written newspapers only. Our results confirm that the number of online published news articles for an earthquake depends on the event magnitude, on the duration of the seismicity linked to the mainshock, on the damage caused, and on the degree of the location’s inhabitation. We propose a general global relationship between the number of news agencies, the earthquake magnitude and the anticipated number of published articles. Such analysis can be used to plan and coordinate global campaigns in cases of relief efforts for disaster-struck areas or educational promotion for earthquake preparedness.