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Record Nr. |
UNISA996418439103316 |
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Titolo |
Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation [[electronic resource] /] / edited by Mehmet Kaya, Şuayip Birinci, Jalal Kawash, Reda Alhajj |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (245 pages) |
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Collana |
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Lecture Notes in Social Networks, , 2190-5428 |
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Disciplina |
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Soggetti |
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Sociophysics |
Econophysics |
Social sciences—Data processing |
Social sciences—Computer programs |
Big data |
Application software |
Data-driven Science, Modeling and Theory Building |
Computational Social Sciences |
Big Data/Analytics |
Computer Appl. in Social and Behavioral Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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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 |
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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. |
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