Upscaling human activity data: A statistical ecology approach.

Big data require new techniques to handle the information they come with. Here we consider four datasets (email communication, Twitter posts, Wikipedia articles and Gutenberg books) and propose a novel statistical framework to predict global statistics from random samples. More precisely, we infer t...

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Autores principales:	Anna Tovo, Samuele Stivanello, Amos Maritan, Samir Suweis, Stefano Favaro, Marco Formentin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f99983cc4a644c4987227f7370badbc8
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id	oai:doaj.org-article:f99983cc4a644c4987227f7370badbc8
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spelling	oai:doaj.org-article:f99983cc4a644c4987227f7370badbc82021-12-02T20:09:49ZUpscaling human activity data: A statistical ecology approach.1932-620310.1371/journal.pone.0253461https://doaj.org/article/f99983cc4a644c4987227f7370badbc82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253461https://doaj.org/toc/1932-6203Big data require new techniques to handle the information they come with. Here we consider four datasets (email communication, Twitter posts, Wikipedia articles and Gutenberg books) and propose a novel statistical framework to predict global statistics from random samples. More precisely, we infer the number of senders, hashtags and words of the whole dataset and how their abundances (i.e. the popularity of a hashtag) change through scales from a small sample of sent emails per sender, posts per hashtag and word occurrences. Our approach is grounded on statistical ecology as we map inference of human activities into the unseen species problem in biodiversity. Our findings may have applications to resource management in emails, collective attention monitoring in Twitter and language learning process in word databases.Anna TovoSamuele StivanelloAmos MaritanSamir SuweisStefano FavaroMarco FormentinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253461 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anna Tovo Samuele Stivanello Amos Maritan Samir Suweis Stefano Favaro Marco Formentin Upscaling human activity data: A statistical ecology approach.
description	Big data require new techniques to handle the information they come with. Here we consider four datasets (email communication, Twitter posts, Wikipedia articles and Gutenberg books) and propose a novel statistical framework to predict global statistics from random samples. More precisely, we infer the number of senders, hashtags and words of the whole dataset and how their abundances (i.e. the popularity of a hashtag) change through scales from a small sample of sent emails per sender, posts per hashtag and word occurrences. Our approach is grounded on statistical ecology as we map inference of human activities into the unseen species problem in biodiversity. Our findings may have applications to resource management in emails, collective attention monitoring in Twitter and language learning process in word databases.
format	article
author	Anna Tovo Samuele Stivanello Amos Maritan Samir Suweis Stefano Favaro Marco Formentin
author_facet	Anna Tovo Samuele Stivanello Amos Maritan Samir Suweis Stefano Favaro Marco Formentin
author_sort	Anna Tovo
title	Upscaling human activity data: A statistical ecology approach.
title_short	Upscaling human activity data: A statistical ecology approach.
title_full	Upscaling human activity data: A statistical ecology approach.
title_fullStr	Upscaling human activity data: A statistical ecology approach.
title_full_unstemmed	Upscaling human activity data: A statistical ecology approach.
title_sort	upscaling human activity data: a statistical ecology approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f99983cc4a644c4987227f7370badbc8
work_keys_str_mv	AT annatovo upscalinghumanactivitydataastatisticalecologyapproach AT samuelestivanello upscalinghumanactivitydataastatisticalecologyapproach AT amosmaritan upscalinghumanactivitydataastatisticalecologyapproach AT samirsuweis upscalinghumanactivitydataastatisticalecologyapproach AT stefanofavaro upscalinghumanactivitydataastatisticalecologyapproach AT marcoformentin upscalinghumanactivitydataastatisticalecologyapproach
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Upscaling human activity data: A statistical ecology approach.

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