Blog clustering
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This document summarizes research on clustering blogs and discovering blog communities. It outlines the significance of clustering the huge and growing blogosphere. Both network-based and content-based clustering approaches are discussed, as well as hybrid approaches. Evaluation of approaches shows hybrid clustering using both network and accompanying content information leads to more coherent blog clusters and distinct communities compared to network-based information alone. The document concludes more work should consider temporal dynamics in blog clustering.
Blog clustering
- 1. School of something Computing FACULTY OF ENGINEERING OTHER Blog Clustering and Community Discovery in the Blogosphere An Overview Ahmad Ammari Research Fellow (User / Community Modelling)
- 2. OUTLINE • Significance • Research Challenges • Network – Based Blog Clustering Approach • Content – Based Blog Clustering Approach • Hybrid – Based Blog Clustering Approach • Evaluation • Conclusion
- 3. The Blogosphere is Huge 100% Growth Rate for every 5 months consistently for the last 4 years Over 120,000 new blogs created every day 1.4 new Blog every second (Technorati, 2009)
- 4. Why Clustering Blogs? • For Bloggers / Readers: o Can focus on the clusters they “belong to” • Improve Recommender Engines: o Suggest related content to other cluster members o Suggest similar bloggers to network / follow
- 5. Why Clustering Blogs? • For Search Engines: o Improve indexing mechanisms o Improve the delivery of the search results by organizing similar results together o Enhance the • Meta Search Engine: Yippy / Clusty navigability of search • Retrieve results from many engines results • Cluster them into 'clouds' based on their contextual contents
- 6. Why Clustering Blogs? • For Sociocultural / Political Studies: o Uncovering trending social, cultural, & political correlations within blogging communities • e.g. Harvard Arab Blogosphere Study, 2009 o Baseline assessment of networked public sphere in Middle East Blogs o Relationships to politics, media, religion, culture, international affairs
- 7. Research Challenges • Existing approaches in webpage clustering & web community discovery are explored in the blogosphere • Applicability Challenges due to Key Differences between the Blogosphere & the Web Blog Posts Web Pages Short-lived References Long-lived References Monitoring Community Relative Temporal Stability Temporal Dynamics Multi-Theme Contents Focused Contents Emergent Text Analysis Traditional Text Analysis Missing Citations Available Citations
- 8. Blog Clusters Vs. Community Discovery • Research Trend: Researchers find it is more prevalent to leverage content information to identify clusters of blog topics and network information to discover blog communities • Proposal: Both content and network information can be used / combined to identify blog Topic clusters and/or blog communities
- 9. Graph – Based Clustering Approach
- 10. Spectral Clustering - Example
- 11. Spectral Clustering - Example
- 12. k-Means Clustering • Assign k centroids Randomly • Assign points to closest centroids • Recalculate and move centroids • Repeat until centroids are stable
- 13. Content – Based Estimation of W • Blog graph could be extremely sparse due to the casual nature 1) -neighbourhood of bloggers • Sparsity Solution: o Edges between blogs are derived using content similarity 2) k Nearest Neighbor kNN • Given: 3) Fully Connected Graph
- 14. Content – Based Clustering Approaches • Blog Contents are used to compute Similarity • Text - Similarity Measure o Cosine Measure • Spherical k-Means o Version of k-means clustering that uses cosine similarity instead of Euclidean similarity
- 15. Content Pre-Processing • Urban Dictionary: http://www.urbandictionary.com/ • Edited by People Acronyms • 5,677,798 definitions since 1999 • Articles (a, an, the ..) • Demonstratives (this, that, these ..) • Conjunctions (for, and, both …) Stop Words Removal • Quantifiers (all, few, many … ) • Prepositions (on ,beneath, over …) • Affix Stemmers e.g indefinitely definite • Porter’s stemmer (Suffix Stripping) Stemming Weighting
- 17. Singular Values as Blog Post Features
- 18. Hybrid - based Clustering approach • Blog Community can be defined as a set of nodes in a graph that link more frequently within this set than outside it and the set shares similar tags (Java et al, 2008)
- 19. Evaluation • Data Set Description • First Data Set: citation network of academic publications o Six categories: Agents, Artificial Intelligence (AI), Databases (DB), Human Computer Interaction (HCI), Information Retrieval (IR) and Machine Learning (ML) o Binary document-term matrix (Presence / Absence of Terms) • Second Data Set: Subgraph of Weblogging Ecosystems (WWE) workshop o Tags fetched from del.icio.us, a well-known social bookmarking site o Corresponding Homepages downloaded • Performed Clustering Performance Comparisons between Hybrid & NCut (Network – based) Approaches
- 20. Tag Distribution in Discovered Communities Top five tags associated with 10 communities found using the Ncut Approach Top five tags associated with 10 communities found using Hybrid Clustering
- 21. Confusion Matrix Comparison NCut Hybrid Average Cluster Similarity NCut Hybrid
- 22. Cluster Similarity Vs AVG Doc Similarity NCut Hybrid
- 23. Conclusion • Both content and network information can be used to identify blog clusters or blog communities • Accompanying content information (user – defined tags, unstructured contents, agglomerative terms / features) with network information lead to better coherent blog clusters and more distinct blog communities than restricted network – based information • Matrix Factorization Techniques (LSA, SVD) reduce Sparsity and High Dimensionality of Content – based Clustering Information whereas Threshold – based filtration techniques are used • There should be more work to be done to consider the temporal dynamics in blog clustering for blogging interaction patterns and community evolutions monitoring
- 24. School of something Computing FACULTY OF ENGINEERING OTHER Thank You Ahmad Ammari Research Fellow (User / Community Modelling)