1. Location-Based Topic Evolution Haiqin Yang, Shouyuan Chen, Michael R. Lyu, Irwin King The Chinese University of Hong Kong 1

2. Outline  Motivation  Location-Based Topic Evolution Model  Experiments  Conclusion 2

3. Location Information is attainable IP GPS 3G, Wi-Fi NFC New Mobile Technologies 3

4. Geo-information  Twitter  Typhoon trajectory estimation  Earthquake location [Sakaki et al.,WWW’10]  Flickr  Geo-tagged photos [Crandall et al., WWW’09]  Geofolk [Sizov, WSDM’10] 4

5. New Applications- Timeliness  Identify users’ interests in a region 5

6. New Applications- Commercial Value  Determine appropriate marketing strategy 6

7. Solution-Topics Learning  Topics: Distributions over words  Location-associated documents  Geo-informaiton with message, posts, tags  Help to learn the topics more accurately 7

8. Current Problems  Do not consider appearance and disappearance of topics  Do not model topic evolution  Have to determine the number of topics  Location-aware Topic Model [Wang et al. GIR’07]  Geofolk [Sizov, WSDM’10]  Geographical topic discovery [Yin et al. WWW’11] 8

9. Our Contributions  Propose a location-based topic evolution (LBTE) model  Model topic changes of users’ interests in a region  Allow for appearance and disappearance of topics  Automatically determine topic numbers  Efficient inference 9

10. Problem Setup  Vocabulary:  Data:  Objective: modeling the topics of data with an unknown number of topics and parameters. 10

11. Assumptions  Documents from unknown topics  Topic from hidden functions, determined by the function value  Functions from a probability measure 11

12. Evolution with Regions  Domains of functions include regions  Values of functions represent topics 12

13. Evolution with Regions and Time  The beginning (end) of function domain correspond to appearance (disappearance) of a topic 13

14. Generative Process 14

15. Inference-Gibbs Sampler 1. Sample auxiliary variables: To determine whether the domain of the function contain the region (Bernoulli) Sample auxiliary variables 2. Sample assignment: Calculate the probability of assigning to existing function and that of assigning to a new function Sample assignment 3. Draw topics parameters 15

16. Experiments  Datasets  Synthetic data  Flickr data  Comparison methods  Dirichlet Process Mixture (DPM)  Location-Based Topic Evolution (LBTE) 16

17. Synthetic Data  Topics Generation  Topics Initialization-Two topics  Center:  Parameter:  Topics Evolution  Die off rate 40%  New topic follows Poisson distribution with parameter 0.8.  Location-associated Documents Generation  10 documents for each topic  Location of each documents follows the uniform distribution at the center of the topic with radius, 5  Values of topics follow 17

18. Results of Synthetic Data  LBTE outperforms the DPM at all the time stamps 18  LBTE recovers true topics and achieves zero variation of information

19. Flickr Data  Geo-tagged photos crawled from 2009/01/01 to 2010/01/01  Only in USA territory. 19 An example { "date": "2009-07-07 19:34:04", "lat": "36.058961", "lon": "- 112.083442", "id": "5919764020", "tags": [ "grandcanyon", "nationalpark", "sunset", "limestone", "scenic"] }

20. Results of National Park  Topics learned from DPM are scattered 20

21. Results of National Park  LBTE utilizes location information and discovers topics based on the regions 21 Yellow Stone Grand Canyon Big Bend Joshua Tree

22. Results of National Park 22

23. Conclusion  Advantages of Location-based Topic Evolution Model  Automatically modeling the number of total topics  Automatically modeling topics’ appearance and disappearance  Succinct sampling-Gibbs sampling 23

24. Thank you ! 24

25. Sample Auxiliary Variables

26. Sample Assignment