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221107 ||| eng |
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|a 9783031082429
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| 100 |
1 |
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|a Özyer, Tansel
|e [editor]
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| 245 |
0 |
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|a Social Media Analysis for Event Detection
|h Elektronische Ressource
|c edited by Tansel Özyer
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2022, 2022
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| 300 |
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|a VI, 229 p. 1 illus
|b online resource
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| 505 |
0 |
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|a Chapter 1. A network-based approach to understanding international cooperation in environmental protection (Andreea Nita) -- Chapter 2. Critical Mass and Data Integrity Diagnostics of a Twitter Social Contagion Monitor (Amruta Deshpande) -- Chapter 3. TenFor: Tool to Mine Interesting Events from Security Forums Leveraging Tensor Decomposition (Risul Islam) -- Chapter 4. Profile Fusion in Social Networks: A Data-Driven Approach -Youcef Benkhedda) -- Chapter 5. RISECURE: Metro Transit Disruptions Detection Using Social Media Mining And Graph Convolution (Omer Zulqar) -- Chapter 6. Local Taxonomy Construction: An Information Retrieval Approach Using Representation Learning (Mayank Kejriwal) -- Chapter 7. The evolution of online sentiments across Italy during first and second wave of the COVID-19 pandemic (Francesco Scotti) -- Chapter 8. Inferring Degree of Localization and Popularity of Twitter Topics and Persons using Temporal Features (Aleksey Panasyuk) -- Chapter 9. Covid-19 and Vaccine Tweet Analysis (Eren Alp)
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| 653 |
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|a Machine learning
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| 653 |
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|a Artificial intelligence / Data processing
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| 653 |
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|a Machine Learning
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| 653 |
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|a Social media
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| 653 |
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|a Graph Theory
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| 653 |
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|a Social Media
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Graph theory
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| 653 |
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|a Natural language processing (Computer science)
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| 653 |
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|a Data Science
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Social Networks
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-08242-9
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-08242-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 005.7
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| 520 |
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|a This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications
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