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

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 n...

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Bibliographic Details
Published in:Social network analysis and mining 2019-12, Vol.9 (1), p.65, Article 65
Main Authors: Interdonato, Roberto, Guillaume, Jean-Loup, Doucet, Antoine
Format: Article
Language:English
Subjects:
Applications of Graph Theory and Complex Networks
Clustering
Computer Science
Data mining
Data Mining and Knowledge Discovery
Document and Text Processing
Earthquakes
Economics
Extraction
Flexibility
Game Theory
Humanitarianism
Humanities
Information retrieval
Language
Law
Lightweight
Management of crises
Methodology of the Social Sciences
Natural disasters
Natural language processing
Original Article
Semantics
Slang
Social and Behav. Sciences
Social networks
Statistical analysis
Statistics for Social Sciences
User behavior
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Summary: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.
ISSN:1869-5450
1869-5469
DOI:10.1007/s13278-019-0608-4