Data Analytics for Social Microblogging Platforms

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. Investigates various methodologies and algorithms for data summarization, clustering and classification Covers both theory and practical applications from around the world, across all related disciplines of Intelligent Information Filtering and Organization Systems Explores different challenges and issues related to spam filtering, attribute selection, and classification for large datasets INDICE: Section 1: Introduction of Intelligent Information Filtering and Organisation Systems for Social Microblogging Sites1. Introduction to Microblogging Sites2. Data structures and data storage3. Data Collection using Twitter APISection 2: Microblogging dataset Applications and Implications4. Brief overview of existing algorithms and ApplicationsAttribute Selection Methods - Filter Method, Wrapper Method, Other attribute selection algorithms5. Spam detection - Spam detection in OSM - Attribute selection for spam detection6.  Summarization - Automatic Document Summarization, Summarization of microblogs, Comparing algorithms for microblog summarization, Summarization Validation7. Cluster Analysis , Clustering Algorithms, Partition based Clustering, Hierarchical Clustering, Density-based Clustering, Graph clustering algorithms, Cluster Validation Indices , Clustering in Online Social Microblogging SitesSection 3: Attribute Selection to Improve Spam Classification8. Introduction of Attribute Selection to Improve Spam Classification9. Attribute Selection Based in Basics of Rough Set Theory and Attribute selection algorithm.10. Experimental Dataset Description11. Evaluating performance  and Evaluation measures12. Fake news, scams, recruiting by terrorist or criminal organizationsSection 4: Microblog Summarization13. Introduction of Microblog Summarization14. Base summarization algorithms15. Unsupervised ensemble summarization approach16. Supervised ensemble summarisation approach17. Experiments and results and Performance analysis18. Demonstrating summarization examplesSection 5: Microblog Clustering19. Introduction of Microblog ClusteringExperimental Dataset - will be posted on Mendeley and link included at end of Chapter 1920. Graph Based Clustering Technique21. Genetic Algorithm based Clustering22. Clustering based on Feature Selection23. Clustering Microblogs using Dimensionality Reduction24. Evaluating performance and result AnalysisSection 6: Conclusion and Future Directions on Social Microblogging Sites

• ISBN: 978-0-323-91785-8
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/09/2022
• Nº Volúmenes: 1
• Idioma: Inglés