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
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
Soumi Dutta works in the Institute of Engineering and Management, Kolkata, West Bengal, India. Asit Kumar Das is Professor of Computer Science and Technology, at the Indian Institute of Engineering Science and Technology Shibpur, Howrah. He is also the Head of the Center of Healthcare Science and Technology of the Institute. His area of research interest includes data mining and pattern recognition, social networks, bioinformatics, machine learning and soft computing, text, audio and video data analysis. Dr. Ghosh is an Assistant Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India. His primary research interests are in social network analysis, legal data analytics, and algorithmic bias and fairness. His research uses techniques from machine learning, natural language processing, information retrieval, and complex network theory. He received his PhD in Computer Science from IIT Kharagpur in 2013. He is a Humboldt Post-doctoral research fellow at the Max Planck Institute for Software Systems (MPI-SWS), Germany. He has also been an Assistant Professor at the Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, India. Debabrata Samanta is an Assistant Professor & Program Head, at the Department of Computing and Information Technologies, Rochester Institute of Technology, Kosovo, Europe. He obtained his Ph.D. in Computer Science and Engg. in the area of SAR Image Processing. He is keenly interested in Interdisciplinary Research and development and has experience spanning fields of SAR Image Analysis, Video surveillance, a Heuristic algorithm for Image Classification, Deep Learning Framework for Detection and Classification, Blockchain, Statistical Modelling, Wireless Adhoc Networks, Natural Language Processing. He has successfully completed six Consultancy Projects. He owns 22 Patents (4 Design Indian Patents and 2 Australian patents Granted, 16 Indian Patents published) and 2 copyrights. He has authored or co-authored over 224 research papers; he has co-authored 13 books and co-edited 13 books. He has presented various papers at international conferences and received Best Paper awards. He is an IEEE Senior Member, an Associate Life Member of the Computer Society of India (CSI), and a Life Member of the Indian Society for Technical Education (ISTE).
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