modeling dynamic multi-topic discussions in online forums
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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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