Twitter Floods when it Rains

Twitter as a tool for the management and analysis of emergency situations: A systematic literature review

“…Once the systematic literature review has been finished, we have analyzed current situation and point out future directions with the aim of fostering and directing further research on the application of Twitter for the management of emergency situations. Saravanou, Valkanas, Gunopulos, and Andrienko (2015) x Li et al (2018) x Graham, Thompson, Wolcott, Pollack, and Tran (2015) x x Antoniadis, Litou, and Kalogeraki (2015) x Wang and Ye (2018) Buribayeva, Miyachi, Yeshmukhametov, and Mikami (2015) x x x Zou, Lam, Cai, and Qiang (2018) x Nar and Akgul (2015) x Onorati, Díaz, and Carrion (2018) x x McCreadie, Macdonald, and Ounis (2015) x Manna and Phongpanangam (2018) x Bakillah, Li, and Liang (2015) x x Hasan, Orgun, and Schwitter (2018) x Ghahremanlou, Sherchan, and Thom (2015) x x Cvetojevic and Hochmair (2018) x Kebabc and Karslgil (2015) x Ragini, Anand, and Bhaskar (2018) x x Temnikova, Castillo, and Vieweg (2015) x Ni et al (2018) x Avvenuti, Vigna, Cresci, Marchetti, and Tesconi (2015) x Wu, Cao, Xiao, and Guo (2018) x x Onorati and Díaz (2015) x Zheng, Han, and Sun (2018) x x…”

A multi-perspective approach for analyzing long-running live events on social media. A case study on the “Big Four” international fashion weeks

“…Inspired by this concept, we consider two categories of bursty topics, namely, irregular and regular. The topics that happen without any specific schedule are irregular bursty topics such as natural hazards (e.g., [91]), accidents [81], and rainy days [92], while regular bursty topics happen regularly and have pre-defined calendars, such as Earth Day. In this section, we introduce LRLEs as examples of regular bursty topics, provide a conceptual overview of their elements, and a highlevel procedure as a road map to studying them.…”

Big Data Approaches for coastal flood risk assessment and emergency response

“…Alongside physical variable datasets, social media provides real‐time information about flooding events (Saravanou, Valkanas, Gunopulos, & Andrienko, ; Smith, Liang, James, Lin, & Liang, ). Analysis of Twitter during flood events in the United Kingdom (Saravanou et al, ), United States (Vieweg, Hughes, Starbird, & Palen, ), Pakistan (Murthy & Longwell, ), Thailand (Kongthon, Haruechaiyasak, Pailai, & Kongyoung, ), and the Philippines (Takahashi, Tandoc Jr, & Carmichael, ) has established the potential for social media as a first response data source, as well as a tool for dissemination of emergency warnings. The value of Twitter data derives from its temporal (immediate) and spatial (geotagging) relation to the event in question (Leetaru, Wang, Padmanabhan, & Shook, ).…”