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 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.
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Subjects: | U10 - Informatique, mathématiques et statistiques, C30 - Documentation et information, crise économique, catastrophe, réseaux sociaux, fouille de textes, fouille de données, analyse de données, traitement des données, traitement de l'information, http://aims.fao.org/aos/agrovoc/c_2470, http://aims.fao.org/aos/agrovoc/c_5082, http://aims.fao.org/aos/agrovoc/c_e64c9a8d, http://aims.fao.org/aos/agrovoc/c_dca12b72, http://aims.fao.org/aos/agrovoc/c_eb9cea5d, http://aims.fao.org/aos/agrovoc/c_15962, http://aims.fao.org/aos/agrovoc/c_10289, http://aims.fao.org/aos/agrovoc/c_3862, |
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dig-cirad-fr-5977672024-01-31T22:08:44Z http://agritrop.cirad.fr/597767/ http://agritrop.cirad.fr/597767/ A lightweight and multilingual framework for crisis information extraction from Twitter data. Interdonato Roberto, Guillaume Jean-Loup, Doucet Antoine. 2019. Social Network Analysis and Mining, 9:65, 20 p.https://doi.org/10.1007/s13278-019-0608-4 <https://doi.org/10.1007/s13278-019-0608-4> A lightweight and multilingual framework for crisis information extraction from Twitter data Interdonato, Roberto Guillaume, Jean-Loup Doucet, Antoine eng 2019 Social Network Analysis and Mining U10 - Informatique, mathématiques et statistiques C30 - Documentation et information crise économique catastrophe réseaux sociaux fouille de textes fouille de données analyse de données traitement des données traitement de l'information http://aims.fao.org/aos/agrovoc/c_2470 http://aims.fao.org/aos/agrovoc/c_5082 http://aims.fao.org/aos/agrovoc/c_e64c9a8d http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_eb9cea5d http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_10289 http://aims.fao.org/aos/agrovoc/c_3862 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/597767/1/10.1007_s13278-019-0608-4.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/s13278-019-0608-4 10.1007/s13278-019-0608-4 info:eu-repo/semantics/altIdentifier/doi/10.1007/s13278-019-0608-4 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/s13278-019-0608-4 |
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U10 - Informatique, mathématiques et statistiques C30 - Documentation et information crise économique catastrophe réseaux sociaux fouille de textes fouille de données analyse de données traitement des données traitement de l'information http://aims.fao.org/aos/agrovoc/c_2470 http://aims.fao.org/aos/agrovoc/c_5082 http://aims.fao.org/aos/agrovoc/c_e64c9a8d http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_eb9cea5d http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_10289 http://aims.fao.org/aos/agrovoc/c_3862 U10 - Informatique, mathématiques et statistiques C30 - Documentation et information crise économique catastrophe réseaux sociaux fouille de textes fouille de données analyse de données traitement des données traitement de l'information http://aims.fao.org/aos/agrovoc/c_2470 http://aims.fao.org/aos/agrovoc/c_5082 http://aims.fao.org/aos/agrovoc/c_e64c9a8d http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_eb9cea5d http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_10289 http://aims.fao.org/aos/agrovoc/c_3862 |
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U10 - Informatique, mathématiques et statistiques C30 - Documentation et information crise économique catastrophe réseaux sociaux fouille de textes fouille de données analyse de données traitement des données traitement de l'information http://aims.fao.org/aos/agrovoc/c_2470 http://aims.fao.org/aos/agrovoc/c_5082 http://aims.fao.org/aos/agrovoc/c_e64c9a8d http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_eb9cea5d http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_10289 http://aims.fao.org/aos/agrovoc/c_3862 U10 - Informatique, mathématiques et statistiques C30 - Documentation et information crise économique catastrophe réseaux sociaux fouille de textes fouille de données analyse de données traitement des données traitement de l'information http://aims.fao.org/aos/agrovoc/c_2470 http://aims.fao.org/aos/agrovoc/c_5082 http://aims.fao.org/aos/agrovoc/c_e64c9a8d http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_eb9cea5d http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_10289 http://aims.fao.org/aos/agrovoc/c_3862 Interdonato, Roberto Guillaume, Jean-Loup Doucet, Antoine A lightweight and multilingual framework for crisis information extraction from Twitter data |
description |
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. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques C30 - Documentation et information crise économique catastrophe réseaux sociaux fouille de textes fouille de données analyse de données traitement des données traitement de l'information http://aims.fao.org/aos/agrovoc/c_2470 http://aims.fao.org/aos/agrovoc/c_5082 http://aims.fao.org/aos/agrovoc/c_e64c9a8d http://aims.fao.org/aos/agrovoc/c_dca12b72 http://aims.fao.org/aos/agrovoc/c_eb9cea5d http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_10289 http://aims.fao.org/aos/agrovoc/c_3862 |
author |
Interdonato, Roberto Guillaume, Jean-Loup Doucet, Antoine |
author_facet |
Interdonato, Roberto Guillaume, Jean-Loup Doucet, Antoine |
author_sort |
Interdonato, Roberto |
title |
A lightweight and multilingual framework for crisis information extraction from Twitter data |
title_short |
A lightweight and multilingual framework for crisis information extraction from Twitter data |
title_full |
A lightweight and multilingual framework for crisis information extraction from Twitter data |
title_fullStr |
A lightweight and multilingual framework for crisis information extraction from Twitter data |
title_full_unstemmed |
A lightweight and multilingual framework for crisis information extraction from Twitter data |
title_sort |
lightweight and multilingual framework for crisis information extraction from twitter data |
url |
http://agritrop.cirad.fr/597767/ http://agritrop.cirad.fr/597767/1/10.1007_s13278-019-0608-4.pdf |
work_keys_str_mv |
AT interdonatoroberto alightweightandmultilingualframeworkforcrisisinformationextractionfromtwitterdata AT guillaumejeanloup alightweightandmultilingualframeworkforcrisisinformationextractionfromtwitterdata AT doucetantoine alightweightandmultilingualframeworkforcrisisinformationextractionfromtwitterdata AT interdonatoroberto lightweightandmultilingualframeworkforcrisisinformationextractionfromtwitterdata AT guillaumejeanloup lightweightandmultilingualframeworkforcrisisinformationextractionfromtwitterdata AT doucetantoine lightweightandmultilingualframeworkforcrisisinformationextractionfromtwitterdata |
_version_ |
1792500119151575040 |