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.

Table of Contents:
Section 1: Introduction of Intelligent Information Filtering and Organisation Systems for Social Microblogging Sites 1. Introduction to Microblogging Sites 2. Data structures and data storage 3. Data Collection using Twitter API Section 2: Microblogging dataset Applications and Implications 4. Brief overview of existing algorithms and Applications Attribute Selection Methods - Filter Method, Wrapper Method, Other attribute selection algorithms 5. Spam detection - Spam detection in OSM - Attribute selection for spam detection 6.  Summarization - Automatic Document Summarization, Summarization of microblogs, Comparing algorithms for microblog summarization, Summarization Validation 7. Cluster Analysis, Clustering Algorithms, Partition based Clustering, Hierarchical Clustering, Density-based Clustering, Graph clustering algorithms, Cluster Validation Indices, Clustering in Online Social Microblogging Sites Section 3: Attribute Selection to Improve Spam Classification 8. Introduction of Attribute Selection to Improve Spam Classification 9. Attribute Selection Based in Basics of Rough Set Theory and Attribute selection algorithm. 10. Experimental Dataset Description 11. Evaluating performance and Evaluation measures 12. Fake news, scams, recruiting by terrorist or criminal organizations Section 4: Microblog Summarization 13. Introduction of Microblog Summarization 14. Base summarization algorithms 15. Unsupervised ensemble summarization approach 16. Supervised ensemble summarisation approach 17. Experiments and results and Performance analysis 18. Demonstrating summarization examples Section 5: Microblog Clustering 19. Introduction of Microblog Clustering Experimental Dataset - will be posted on Mendeley and link included at end of Chapter 19 20. Graph Based Clustering Technique 21. Genetic Algorithm based Clustering 22. Clustering based on Feature Selection 23. Clustering Microblogs using Dimensionality Reduction 24. Evaluating performance and result Analysis Section 6: Conclusion and Future Directions on Social Microblogging Sites