Developing Seismic Intensity Maps From Twitter Data; The Case Study of Lesvos, Greece 2017 Earthquake: Assessments, Improvements and Enrichments on the Methodology

Abstract

<p><strong>Abstract.</strong> This article presents an effort to validate and further improve a previously published innovative approach for drawing macroseismic intensity maps from data extracted from sources of volunteered geographic information (VGI). Our approach involves classification of macroseismic observations (extracted from social media sources) to values of the EMS 98 intensity scale, leading to the drawing of isoseismal maps. The earthquake of June 12th, 2017 (Mw 6.3) that occurred off the south coast of Lesvos Island, Greece, was used as a case study; its main shock was located at depth of about 13 km. This specific event, which claimed the life of a woman and caused at least 15 injuries due to collapsing buildings and falling debris (mainly in the town of Vrissa), was chosen for the specific geomorphological characteristics of the meizoseismal area, time of occurrence and distribution of damage. Twitter was chosen as a VGI source mostly for reasons of consistency with the original published work, generating comparable findings that can be assessed more readily to facilitate further development of the methodology. Results of the dataset analysis include the drawing of the isoseismal maps from Tweets published within different time periods (6 h, 12 h, 24 h, 48 h); and the identification of various text patterns regarding the evaluation of the macroseismic observations that result into intensity values. The present work offers additional empirical evidence regarding the validity of the methodology presented in the scientific literature, and further enriches it by providing additional text patterns and specific improvements related to the classification of the information in certain values of seismic intensity. Assessment of the results is enriched by the progress that has been noted in the field and has been presented in the international scientific literature since 2016.</p>

Social Network Users Create Seismic Intensity Maps: An Automatic Approach of the Methodology

“…The main innovation of current research is the presentation of a structured automatic approach for producing seismic intensity maps solely through social media data. This approach describes the current state-of-the-art of a methodology that was presented previously and was based on manual processing [5]. Current status demonstrates significantly hopeful signs that effective automation on social media data, can lead to effective exploitation of enormous, in terms of size, information, in benefit of seismic intensity mapping.…”

“…Apart from the tweets, a second dataset, consisted of word patterns that were linked to seismic intensity values of the European Macroseismic Scale (EMS98) was used. As already mentioned, the word patterns had been identified during previous research [4][5][6], had been also justified according to the official EMS98 description [19] and are displayed in Table 1. In cases that a certain word pattern could fit in a range of values, instead of one certain (i.e.…”

“…Additionally, the author, during the latest years, researched on manipulating exclusively social media information for generating seismic intensity maps, emerging thus the usefulness of the enormous, in terms of quantity, dynamic content that is constantly generated [3,4,5]. The output of author's previous research led, among others, to the identification of word patterns that can be used to classify the texts of social media posts according to the values of the macroseismic intensity scale.…”

Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China

“…For example, Shuai et al [ 19 ] conducted spatial interpolation analysis to determine the extent of the seismic influence field based on user location information. Arapostathis et al [ 20 ] used earthquake-related tweets with location information to create an isoseismal map on the southern coast of the Greek island of Lesvos. Taking the 2017 Jiuzhaigou earthquake as an example, Xing et al [ 21 ] analyzed relevant spatiotemporal and semantic information from microblogs.…”