Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scient...

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Detalles Bibliográficos
Autores principales: Emmert-Streib, Frank, Yli-Harja, Olli P., Dehmer, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931880/
https://www.ncbi.nlm.nih.gov/pubmed/33693318
http://dx.doi.org/10.3389/fdata.2018.00002
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author Emmert-Streib, Frank
Yli-Harja, Olli P.
Dehmer, Matthias
author_facet Emmert-Streib, Frank
Yli-Harja, Olli P.
Dehmer, Matthias
author_sort Emmert-Streib, Frank
collection PubMed
description Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems.
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spelling pubmed-79318802021-03-09 Data Analytics Applications for Streaming Data From Social Media: What to Predict? Emmert-Streib, Frank Yli-Harja, Olli P. Dehmer, Matthias Front Big Data Big Data Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems. Frontiers Media S.A. 2018-09-11 /pmc/articles/PMC7931880/ /pubmed/33693318 http://dx.doi.org/10.3389/fdata.2018.00002 Text en Copyright © 2018 Emmert-Streib, Yli-Harja and Dehmer. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Emmert-Streib, Frank
Yli-Harja, Olli P.
Dehmer, Matthias
Data Analytics Applications for Streaming Data From Social Media: What to Predict?
title Data Analytics Applications for Streaming Data From Social Media: What to Predict?
title_full Data Analytics Applications for Streaming Data From Social Media: What to Predict?
title_fullStr Data Analytics Applications for Streaming Data From Social Media: What to Predict?
title_full_unstemmed Data Analytics Applications for Streaming Data From Social Media: What to Predict?
title_short Data Analytics Applications for Streaming Data From Social Media: What to Predict?
title_sort data analytics applications for streaming data from social media: what to predict?
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931880/
https://www.ncbi.nlm.nih.gov/pubmed/33693318
http://dx.doi.org/10.3389/fdata.2018.00002
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