Modeling dynamic multi topic discussions in online forums
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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.

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Modeling dynamic multi topic discussions in online forums

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
Social Media

  • Web 2.0 applications socialize users online

  • Online Forums

    • Distinct platform for knowledge sharing and information exchange

Reveal how information propagates on Internet.

Modeling the process of topic discussions and predicting user activity is an interesting problem!


Benefits of modeling
Benefits of Modeling

  • Understand online human interactions and group forming

  • Improve applications e.g., recommender

  • Track new ideas and technology

  • Mine opinions about products

Social network analysis

User review


Environment of online forums
Environment of Online Forums

  • Great complexity

  • Randomness

    • Usually no well-defined friendships or co-authorships

    • Free to posting

    • Topic drifts in a single thread

What are the mechanisms underlying user’s participation

433,839 threads

13,599,245 posts

From which perspective to view the process of topic discussion

?

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

Modeling Dynamic Multi-topic Discussions is challenging !

How to measure the importance of a user in discussions


Outline
Outline

  • Motivation and Intuitions

  • Topic Flow Models

  • Experimental Results

  • Summary


Topic flow model tfm
Topic Flow Model (TFM)

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

Reply Link

Topic Flow

Topic diffuses through the underlying social networks

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


Basic topic flow model b tfm
Basic Topic Flow Model (B-TFM)

Thread Document:

Frequency of :

Frequency of :

Social Network

Thread Documents

Peer-influence

Topic Flow

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

Self-preference

Normalization

Random Walk With Restart


Topic specific tfm t tfm
Topic-specific TFM (T-TFM)

  • Different interaction patterns according to different topics

iPhone

FIFA World Cup

Using Latent Dirichlet Allocation [Blei 2003]


Time sensitive t tfm tt tfm
Time-sensitive T-TFM (TT-TFM)

  • Forgetting Mechanism

past

now

Time lapses

now

Time Lapse Factor


Evaluation prediction
Evaluation: Prediction

  • ParticipationRank (indicator)

    • The willingness of a user in participation to discussion of a topic

Train

Predict

?

Ranking

Whether a user joins in discussion? (post at least once )

Synthesize For T-TFM and TT-TFM


Outline1
Outline

  • Motivation and Intuitions

  • Topic Flow Models

  • Experimental Results

  • Summary


Experiments
Experiments

  • Dataset (www.honda-tech.com)

    • Two communities: Drag Racing and Honda/Acura

    • Across one year, from 09/01/2008 to 08/31/2009.

posted more than the average number of posts per user.


Results
Results

  • 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
Model Selection

  • = 0.3 and 0.1

  • T = 30 and 40

  • = 0.01


Summary
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 onprediction of user activity

  • Future work:

    • Utilize the web structure of online forum

    • More data sets e.g.,

    • Build recommendation system


Thanks!

Any Question?


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