1149048
doi
10.5281/zenodo.1149048
oai:zenodo.org:1149048
user-eu
Rossi, Claudio
Istituto Superiore Mario Boella
Casetti, Claudio
Politecnico di Torino
Angaramo, Federico
Istituto Superiore Mario Boella
A Language-agnostic Approach to Exact Informative Tweets during Emergency Situations
Longhini, Jacopo
Politecnico di Torino
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Disaster relief
social media analysis
classification
machine learning
real-world traces
<p>In this paper, we propose a machine learning approach to automatically classify non-informative and informative contents shared on Twitter during disasters caused by natural hazards. In particular, we leverage on previously sampled and labeled datasets of messages posted on Twitter during or in the aftermath of natural disasters. Starting from results obtained in previous studies, we propose a language-agnostic model. We define a base feature set considering only Twitter-specific metadata of each tweet, using classification results from this set as a reference. We introduce an additional feature, called the Source Feature, which is computed considering the device or platform used to post a tweet, and we evaluate its contribution in improving the classifier accuracy.</p>
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Zenodo
2017-12-11
info:eu-repo/semantics/conferencePaper
1149047
user-eu
award_title=Improving Resilience to Emergencies through Advanced Cyber Technologies; award_number=700256; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/700256; funder_id=00k4n6c32; funder_name=European Commission;
1579542185.244409
925157
md5:a014218f0c5987fe87ec215569ab0a3b
https://zenodo.org/records/1149048/files/13_Longhini_et_al_2017_DSEM.pdf
public
10.5281/zenodo.1149047
isVersionOf
doi