Data Analytics for Social Microblogging Platforms

Provides advanced coverage of social media data engineering

Soumi Dutta (Author), Asit Kumar Das (Author), Saptarshi Ghosh (Author), Debabrata Samanta (Author)

9780323917858, Elsevier Science

Paperback / softback, published 9 November 2022

328 pages
22.9 x 15.2 x 2.1 cm, 0.52 kg

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.

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

Subject Areas: Artificial intelligence [UYQ], Information technology: general issues [UB]