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Putting social media and networking data in practice for education, planning, prediction and recommendation
This book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers suppor...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
Springer
2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-030-33698-1 http://cds.cern.ch/record/2706759 |
_version_ | 1780964884686569472 |
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author | Kaya, Mehmet Birinci, Şuayip Kawash, Jalal Alhajj, Reda |
author_facet | Kaya, Mehmet Birinci, Şuayip Kawash, Jalal Alhajj, Reda |
author_sort | Kaya, Mehmet |
collection | CERN |
description | This book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers supporting students, food venue, friend and paper recommendation to demonstrate the power of social media data analysis. Secondly, this book treats behavior analysis and understanding as important for a variety of applications, including inspiring behavior from discussion platforms, determining user choices, detecting following patterns, crowd behavior modeling for emergency evacuation, tracking community structure, etc. Third, fraud and anomaly detection have been well tackled based on social media analysis. This has is illustrated in this book by identifying anomalous nodes in a network, chasing undetected fraud processes, discovering hidden knowledge, detecting clickbait, etc. With this wide coverage, the book forms a good source for practitioners and researchers, including instructors and students. |
id | cern-2706759 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
publisher | Springer |
record_format | invenio |
spelling | cern-27067592021-04-21T18:11:47Zdoi:10.1007/978-3-030-33698-1http://cds.cern.ch/record/2706759engKaya, MehmetBirinci, ŞuayipKawash, JalalAlhajj, RedaPutting social media and networking data in practice for education, planning, prediction and recommendationComputing and ComputersThis book focusses on recommendation, behavior, and anomaly, among of social media analysis. First, recommendation is vital for a variety of applications to narrow down the search space and to better guide people towards educated and personalized alternatives. In this context, the book covers supporting students, food venue, friend and paper recommendation to demonstrate the power of social media data analysis. Secondly, this book treats behavior analysis and understanding as important for a variety of applications, including inspiring behavior from discussion platforms, determining user choices, detecting following patterns, crowd behavior modeling for emergency evacuation, tracking community structure, etc. Third, fraud and anomaly detection have been well tackled based on social media analysis. This has is illustrated in this book by identifying anomalous nodes in a network, chasing undetected fraud processes, discovering hidden knowledge, detecting clickbait, etc. With this wide coverage, the book forms a good source for practitioners and researchers, including instructors and students.Springeroai:cds.cern.ch:27067592020 |
spellingShingle | Computing and Computers Kaya, Mehmet Birinci, Şuayip Kawash, Jalal Alhajj, Reda Putting social media and networking data in practice for education, planning, prediction and recommendation |
title | Putting social media and networking data in practice for education, planning, prediction and recommendation |
title_full | Putting social media and networking data in practice for education, planning, prediction and recommendation |
title_fullStr | Putting social media and networking data in practice for education, planning, prediction and recommendation |
title_full_unstemmed | Putting social media and networking data in practice for education, planning, prediction and recommendation |
title_short | Putting social media and networking data in practice for education, planning, prediction and recommendation |
title_sort | putting social media and networking data in practice for education, planning, prediction and recommendation |
topic | Computing and Computers |
url | https://dx.doi.org/10.1007/978-3-030-33698-1 http://cds.cern.ch/record/2706759 |
work_keys_str_mv | AT kayamehmet puttingsocialmediaandnetworkingdatainpracticeforeducationplanningpredictionandrecommendation AT birincisuayip puttingsocialmediaandnetworkingdatainpracticeforeducationplanningpredictionandrecommendation AT kawashjalal puttingsocialmediaandnetworkingdatainpracticeforeducationplanningpredictionandrecommendation AT alhajjreda puttingsocialmediaandnetworkingdatainpracticeforeducationplanningpredictionandrecommendation |