Extracting localized information from a Twitter corpus for flood prevention
In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial perspective, our goal here is to get a first estimation of the quality and precision of the geographical information featured in the collected corpus. From a topical perspective, we discuss the representation of Twitter posts, and strategies to process an initially unlabeled corpus of tweets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning
Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the prob…
Decision MakingGeneral ClassificationLanguage ModelingLanguage Modelling+3Computing flood probabilities using Twitter: application to the Houston urban area during Harvey
In this paper, we investigate the conversion of a Twitter corpus into geo-referenced raster cells holding the probability of the associated geographical areas of being flooded. We describe a baseline approach that combin…
regressionFinding Relevant Flood Images on Twitter using Content-based Filters
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures. At the same time, a plethora of information is buried in an abun…
Building a Crisis Management Term Resource for Social Media: The Case of Floods and Protests
Extracting information from social media is being currently exploited for a variety of tasks, including the recognition of emergency events in Twitter. This is done in order to supply Crisis Management agencies with addi…
DescriptiveInformation RetrievalManagementNamed Entity Recognition (NER)+1Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style
We present results of the first gender classification experiments on Slovene text to our knowledge. Inspired by the TwiSty corpus and experiments (Verhoeven et al., 2016), we employed the Janes corpus (Erjavec et al., 20…
Gender ClassificationGeneral ClassificationLEMMALemmatization