1. Qingxia Liu qxliu.nju@gmail.com
Interactive Hierarchical Tag Clouds for Summarizing Spatiotemporal Social Contents
[ICDE 2014] Kang, Wei, Anthony KH Tung, Feng Zhao, and Xinyu Li
Qingxia Liu

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Introduction
Goal
interactive exploration of regions by summarizing and browsing social network contents
Input
Social network contents: textual microblogs e.g. tweets
User specified time, region
Output
Topic hierarchies
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3. Vesta: http://db128gb-b.ddns.comp.nus.edu.sg/kangwei/bicluster/

4. Partition-and-merge Scheme in Vesta
biclustering
time
latitude
longitude
hLDA
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Biclustering
Bicluster (T, C)
Cluster in two directions (tags, contents)
Density: non-zero rate of corresponding submatrix
Formal context (A, O, I) -- bicluster
Fullness: 所对应的矩阵全为1
Maximum: 再添如任一行或列都将引入0值
Galois operations A’, A’’
Biclustering 双聚类、联合聚类
Formal Concept Analysis （FCA） 形式概念分析
Fullness: 任意a，o属于A,O, (a,o)∈I，即任意一对a,o均存在关系I
Maximum：新加入任何a或者o都会引入不存在关系I的a,o对
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Online Merging
Merging Biclusters
sharing most common tags
δden = # of 1s/ # of total entries
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7. Online Merging Merging Topic Hierarchies
Given: m topic hierarchies, # of levels n0, # of tags for each level of the result
Each tag only appears in one level
Weight function

9. Evaluation
Performance

10. Evaluation
Precision & Recall
Offline scalability

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Conclusion
Contributions
Summary generation by biclustering
Extended disk-based PM scheme
Topic hierarchies by hLDA and merging
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v.s. RDF data management
Similarities
Collection of information containers
tweets v.s. entity descriptions
Can be clustered into topics
Differences
Independent plain texts v.s. Linked entities
Feature : keywords v.s. property,value
Future Work
Entity Feature extraction
Topic generation
Link summarization
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Thank You !
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