Open AccessA geographical approach for on-the-fly prioritizing social-media messages for flood risk management based on sensor data
Author(s) -
Luiz Fernando Ferreira Gomes de Assis,
Flávio Horita,
Benjamin Herfort,
Enrico Steiger,
João Porto de Albuquerque
Publication year - 2015
Publication title -
anais do brazilian workshop on social network analysis and mining (brasnam)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/brasnam.2015.6768
Subject(s) - computer science , flood myth , complement (music) , social media , data science , data anonymization , confidentiality , trustworthiness , data mining , risk analysis (engineering) , computer security , information privacy , world wide web , business , geography , biochemistry , chemistry , archaeology , complementation , gene , phenotype
Flood risk management requires updated and accurate information about the overall situation in vulnerable areas. Social media messages are considered to be as a valuable additional source of information to complement authoritative data (e.g. in situ sensor data). In some cases, these messages might also help to complement unsuitable or incomplete sensor data, and thus a more complete description of a phenomenon can be provided. Nevertheless, it remains a difficult matter to identify information that is significant and trustworthy. This is due to the huge volume of messages that are produced and which raises issues regarding their authenticity, confidentiality, trustworthiness, ownership and quality. In light of this, this paper adopts an approach for on-the-fly prioritization of social media messages that relies on sensor data (esp. water gauges). A proof-of-concept application of our approach is outlined by means of a hypothetical scenario, which uses social media messages from Twitter as well as sensor data collected through hydrological stations networks maintained by Pegelonline in Germany. The results have shown that our approach is able to prioritize social media messages and thus provide updated and accurate information for supporting tasks carried out by decision-makers in flood risk management.