Spatial-Temporal Analysis of Social Media Data Related to Nepal Earthquake 2015

Abstract

Social Medias these days have become the instant communication platform to share anything; from personal feelings to the matter of public concern, these are the easiest and aphoristic way to deliver information among the mass. With the development of Web 2.0 technologies, more and more emphasis has been given to user input in the web; the concept of Geoweb is being visualized and in the recent years, social media like Twitter, Flicker are among the popular Location Based Social Medias with locational functionality enabled in them. Nepal faced devastating earthquake on 25 April, 2015 resulting in the loss of thousands of lives, destruction in the historical-archaeological sites and properties. Instant help was offered by many countries around the globe and even lots of NGOs, INGOs and people started the rescue operations immediately; concerned authorities and people used different communication medium like Frequency Modulation Stations, Television, and Social Medias over the World Wide Web to gather information associated with the Quake and to ease the rescue activities. They also initiated campaign in the Social Media to raise the funds and support the victims. Even the social medias like Facebook, Twitter, themselves announced the helping campaign to rebuild Nepal. In such scenario, this paper features the analysis of Twitter data containing hashtag related to Nepal Earthquake 2015 together with their temporal characteristics, when were the message generated, where were these from and how these spread spatially over the internet?

Understanding #WorldEnvironmentDay User Opinions in Twitter: A Topic-Based Sentiment Analysis Approach

“…Table 2 was created by the authors based on Thapa [ 36 ] and Pena [ 37 ] to show the most used hashtags when earthquakes were produced in different parts of the world. In the case of the Mexican earthquake, the hashtags #sismo and #FuerzaMexico were the most frequently used, highlighting thus the trending topics in that country throughout the year 2017.…”

“…Regarding the earthquake that hit Haiti in 2010 and shocked the world, #Haiti was the most shared hashtag throughout the world during that year. Furthermore, with respect to the earthquake in Nepal, during the month following the catastrophe, 33,610 tweets were published with the hashtag #nepalearthquake [ 36 ]. These figures show how the comments are grouped with respect to a topic around a series of hashtags shared by other users and can be studied to obtain information [ 30 ].…”

Information Preference and Information Supply Efficiency Evaluation before, during, and after an Earthquake: Evidence from Songyuan, China

“…Field investigation revealed disaster information needs in detail, but the timeliness and regional range of acquiring disaster information were limited [ 82 ]. Future methods of acquiring data may consider combining field investigation with big data mining, such as using data extracted from social media [ 24 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ], Google Trends [ 92 , 93 ], and Baidu Index [ 94 , 95 ]. In particular, these several data sources can make it possible to quite easily track the behavior of Internet users in real time.…”

Twitter, disasters and cultural heritage: A case study of the 2015 Nepal earthquake

“…Researchers have focused on Twitter, mobile applications and crowdsourced mapping both during the disaster response and recovery phase. Thapa (2016) analysed location‐based tweets posted during the 2015 Nepal earthquake to understand the spatial and temporal characteristics of the tweets. The study concluded that a relatively small number (22%) of people used hashtags related to the event, whereas most of the tweets were without any hashtag.…”