Mining Twitter Data for Landslide Events Reported Worldwide

Abstract

The explosion of user generated content in social media published from mobile devices has led to the concept known as “citizen sensing.” Although English has been adopted by many as a de facto standard international language, reports about events, such as disasters, are frequently provided by citizens in their local language in addition to English. Attempting to integrate citizen reports from many languages is a significant challenge. This article describes the tools that address this challenge to enable the support of citizen-sensing of landslide events reported worldwide. Multilingual support is based on the first unified cross-lingual dataset of word vectors for representing texts in multiple languages. The classification model based on the proposed cross-lingual word vectors outperforms the “native” and “translated” approaches based on monolingual word vectors. Furthermore, it does not require the creation of a separate training set in a local language or its translation to English.

Review Article: Leveraging Social Media for Managing Natural Hazard Disasters: A Critical Review of Data Collection Strategies and Actionable Insights

“…", we analyse the articles listed in the Social Media Literature Database under the theme of "Disaster Management". By studying various articles (Palen et al, 2010;Sakaki et al, 2010;Zhou et al, 2013;Jongman et al, 2015;Musaev et al, 2018;Phengsuwan et al, 2019;Guntha et al, 2020b;Gopal et al, 2020;Guntha et al, 2020a;Gopal et al, 2022;Aswathy et al, 2022) and based on our experience, we have defined nine generic actionable information categories which will be assigned to each article under the "Disaster management" theme listed in the literature database. 1 displays the various categories with their respective descriptions, detailing the methods and applications considered within each AI category in this study.…”

“…The issue of reliability is a significant concern when using social media data for disaster management (Mazoyer et al, 2018;Liu et al, 2020). To address this concern, many authors incorporated external data sources alongside social media data, aiming to enhance the efficiency and reliability of their methodologies (Chatfield and Brajawidagda, 2013;Joseph et al, 2014;Musaev et al, 2018).…”

“…Out of the 250 articles in the Social Media Literature Database, 26% (65 articles) integrated external data to enhance their methodologies. Notably, the majority of external data utilisation was observed in articles focusing on events in the USA, where FEMA and USGS data were frequently employed (Hodas et al, 2015;Liu et al, 2018;Musaev et al, 2018).…”

“…The articles listed in the literature database predominantly explore hurricanes and floods (Le Coz et al, 2016;Madichetty, 2020), neglecting other impactful events such as pandemics, landslides, storms, and cyclones, which are few (Islam and Walkerden, 2015;Musaev et al, 2018). This may overlook crucial aspects of disaster dynamics (see section 5.2).…”

“…Inaccurate information may be disseminated, whether intentionally or unintentionally, including the spread of rumours or discussions about similar disaster events occurring elsewhere (Remy et al, 2013;Musaev et al, 2018;Arapostathis, 2021). This challenge underscores the difficulty in identifying relevant data that can be considered actionable.…”