Location-Based Topic Evolution

Haiqin Yang, Shouyuan Chen, Michael R. Lyu, Irwin King

The Chinese University of Hong Kong

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Outline

Motivation Location-Based Topic Evolution Model Experiments Conclusion

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Location Information is attainable IPGPS3G, Wi-FiNFC

New Mobile Technologies

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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]

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New Applications-Timeliness

Identify users’ interests in a region

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New Applications-Commercial Value

Determine appropriate marketing strategy

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Solution-Topics Learning

Topics: Distributions over words Location-associated documents

Geo-informaiton with message, posts, tags

Help to learn the topics more accurately

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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

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

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Problem Setup

Vocabulary: Data:

Objective: modeling the topics of data with an unknown number of topics and parameters.

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Assumptions

Documents from unknown topics Topic from hidden

functions, determined by the function value

Functions from a probability measure

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Evolution with Regions

Domains of functions include regions Values of functions represent topics

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Evolution with Regions and Time

The beginning (end) of function domain correspond to appearance (disappearance) of a topic

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Generative Process

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Inference-Gibbs Sampler

1. Sample auxiliary variables: To determine whether the domain of the function contain the region (Bernoulli)

2. Sample assignment: Calculate the probability of assigning to existing function and that of assigning to a new function

3. Draw topics parameters15

Experiments

Datasets Synthetic data Flickr data

Comparison methods Dirichlet Process Mixture (DPM) Location-Based Topic Evolution (LBTE)

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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

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Results of Synthetic Data

LBTE outperforms the DPM at all the time stamps

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LBTE recovers true topics and achieves zero variation of information

Flickr Data

Geo-tagged photos crawled from 2009/01/01 to 2010/01/01

Only in USA territory.

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An example{ "date": "2009-07-07 19:34:04", "lat": "36.058961", "lon": "-112.083442", "id": "5919764020", "tags": [ "grandcanyon", "nationalpark", "sunset", "limestone", "scenic"] }

Results of National Park

Topics learned from DPM are scattered

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Results of National Park LBTE utilizes location information and

discovers topics based on the regions

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Yellow Stone

Grand Canyon

Big Bend

Joshua Tree

Results of National Park

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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

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Thank you !

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Sample Auxiliary Variables

Sample Assignment