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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2019

01.12.2019 | Original Article

A lightweight and multilingual framework for crisis information extraction from Twitter data

verfasst von: Roberto Interdonato, Jean-Loup Guillaume, Antoine Doucet

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2019

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Abstract

Obtaining relevant timely information during crisis events is a challenging task that can be fundamental to handle the consequences deriving from both unexpected events (e.g., terrorist attacks) and partially predictable ones (i.e., natural disasters). Even though microblogging-based online social networks (e.g., Twitter) have become an attractive data source in these emergency situations, overcoming the information overload deriving from mass events is not trivial. The aim of this work was to enable unsupervised extraction of relevant information from Twitter data during a crisis event, offering a lightweight alternative to learning-based approaches. The proposed lightweight crisis management framework integrates natural language processing and clustering techniques in order to produce a ranking of tweets relevant to a crisis situation based on their informativeness. Experiments carried out on six Twitter collections in two languages (English and French) proved the significance and the flexibility of our approach.
Vorheriger Artikel Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks
Nächster Artikel PLinkSHRINK: a parallel overlapping community detection algorithm with Link-Graph for large networks

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Fußnoten
5
Since we focus on a Twitter case, but the approach is generalizable to other OSNs, we will use the terms microblog posts and tweets interchangeably.
 
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Metadaten
Titel
A lightweight and multilingual framework for crisis information extraction from Twitter data
verfasst von
Roberto Interdonato
Jean-Loup Guillaume
Antoine Doucet
Publikationsdatum
01.12.2019
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2019
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-019-0608-4

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