Modeling Dynamic Multi-topic Discussions in Online Forums

Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen*

Zhejiang University, China*Zhejiang Health Information Center, China

July 13, AAAI’2010

Atlanta, GA, USA

Social Media• Web 2.0 applications socialize users online

• Online Forums– Distinct platform for knowledge sharing and information exchange

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Reveal how information propagates on Internet.Modeling the process of topic discussions and predicting user activity is an interesting problem!

Benefits of Modeling

• Understand online human interactions and group forming

• Improve applications e.g., recommender• Track new ideas and technology• Mine opinions about products

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Social network analysis

User review

Environment of Online Forums

• Great complexity

• Randomness– Usually no well-defined

friendships or co-authorships– Free to posting– Topic drifts in a single thread

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?

What are the mechanisms underlying user’s participation

What are the mechanisms underlying user’s participation

From which perspective to view the process of topic discussion

From which perspective to view the process of topic discussion

How to make use of the property of topics and temporal feature for modeling

How to make use of the property of topics and temporal feature for modeling

How to measure the importance of a user in discussions

How to measure the importance of a user in discussions

Modeling Dynamic Multi-topic Discussions is challenging !

433,839 threads13,599,245 posts

433,839 threads13,599,245 posts

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Outline

• Motivation and Intuitions

• Topic Flow Models

• Experimental Results

• Summary

Topic Flow Model (TFM)

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Reply LinkTopic Flow

The new comer reads some of the previous comments before posting.

The information (topic) flows from early participant to late participant .

Topic diffuses through the underlying social networks

Basic Topic Flow Model (B-TFM)

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Thread Document: Frequency of :Frequency of :

d D

Topic Flow

Peer-influence

dijRdiC

Self-preference

Normalization

ParticipationRank: measures the susceptibility of a user to a ‘infective’ topic

Social Network

Thread Documents

D

Random Walk With Restart

i ji

dij ijd Dw R

di id Dy C

1 (1 ) /T n S D W 11/iyq y

( 1) (1 )Tt t p S p q* 1(1 )( )T p I S q

j

Topic-specific TFM (T-TFM)

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FIFA World Cup

iPhone

Using Latent Dirichlet Allocation [Blei 2003]( | )z d

ij ijd Dw P z d R

( | )z di id Dy P z d C

Different interaction patterns according to different topics

Time-sensitive T-TFM (TT-TFM)

• Forgetting Mechanism

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past now

Time lapses

nowTime Lapse Factor

exp( ) ( | )z dij d ijd Dw t P z d R

exp( ) ( | )z di d id Dy t P z d C

Evaluation: Prediction

• ParticipationRank (indicator)– The willingness of a user in participation to

discussion of a topic

10Synthesize For T-TFM and TT-TFM

?

Train Predict

Ranking

p

* *( | )F

zz Z d DP z d

p p Whether a user

joins in discussion? (post at least once )

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Outline

• Motivation and Intuitions

• Topic Flow Models

• Experimental Results

• Summary

Experiments

• Dataset (www.honda-tech.com)

– Two communities: Drag Racing and Honda/Acura– Across one year, from 09/01/2008 to 08/31/2009.

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posted more than the average number of posts per user.

Results

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• Evaluations– Divide the data into 12 continuous time windows– Generate ranking for each one month data, and

predict user posting activity in the following one week

Model Selection

• = 0.3 and 0.1

• T = 30 and 40

• = 0.01

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Summary

• An intuitive model of discussions in online forums• Topic Flow Models (TFM)

– Consider both peer-influence and self-preference

– Property of latent topics

– Temporal feature: forgetting mechanism

• Evaluation on prediction of user activity • Future work:

– Utilize the web structure of online forum

– More data sets e.g.,

– Build recommendation system15

Thanks!

Any Question?

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