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Retweet Prediction with Attention-
based Deep Neural Network
#CIKM2016
Authors: Qi Zhang, Yeyun Gong, Jindou Wu, Haoran Huang, Xuanjing Huang
Reading group: 25/10/2017
Presenter: Guangyuan Piao (Unit for Social Semantics)
Mentor: Subhasis Thakur | Supervisor: John G. Breslin
Agenda
•  Background & Related Work
•  Proposed Approach
•  Experimental Setup & Results
•  Conclusions
•  Summary
2
Background
3	
•  easy real-time information sharing
•  1 billion unique visits / month for Twitter
Background – Retweeting Behavior
4
Background – Retweeting Behavior
5	
•  key mechanism for spreading information
•  can help information spreading prediction, popularity prediction etc.
(Some) Related Work
6	
•  Retweeting behavior
•  study of a number of features for retweetability of tweets [Suh et al., SocialCom’10]
•  feature-aware factorization model [Feng et al, WSDM’13]
•  considering information about user, tweet, and author
•  who will retweet me? [Luo et al., SIGIR’13]
•  using learning-to-rank framework
•  non-parametric statistical models [Zhang et al. AAAI’15]
•  combining structural, textual & temporal info.
(Some) Related Work
7	
•  Retweeting behavior
•  study of a number of features for retweetability of tweets [Suh et al., SocialCom’10]
•  feature-aware factorization model [Feng et al, WSDM’13]
•  considering information about user, tweet, and author
•  who will retweet me? [Luo et al., SIGIR’13]
•  using learning-to-rank framework
•  non-parametric statistical models [Zhang et al. AAAI’15]
•  combining structural, textual & temporal info.
feature engineering is required
(Some) Related Work
8	
•  Convolutional Neural Network (CNN)
•  image recognition
•  video processing
•  natural language processing
•  Attention-based Neural Network
•  machine translation
•  speech recognition
•  visual object classification
Proposed Approach – Variants of CNN approach
9	
words of a tweet
•  Vu: user embedding vector
•  Vp: tweet embedding vector
Proposed Approach – Variants of CNN approach
10	
•  Vu: user embedding vector
•  Vp: tweet embedding vector
•  Va: author embedding vector
Proposed Approach
11	
•  Modeling User Interests based on Tweet History [t1, t2 … tm]
•  clustering m tweets of each user into n groups using K-means
•  using the central tweet of each group as an interest of user
•  user interest profile [t1, t2 … tn]
•  Modeling User Interests based on Tweet History [t1, t2 … tm]
•  clustering m tweets of each user into n groups
•  using the central tweet of each group as an interest of user
•  user interest profile [t1, t2 … tn]
•  apply CNN for each tweet to obtain
tweet embeddings
Proposed Approach
12
Proposed Approach
13	
•  Attention
•  Folding
the value in the i-th position of the embedding of the j-th attention interests
Proposed Approach
14
Proposed Approach
15	
•  Vu: user embedding vector
•  Vi: user interest embedding vector
•  S: similarity(user interest vector, tweet vector)
•  Vp: tweet embedding vector
•  Va: author embedding vector
Experiment Setup
16	
•  Twitter Dataset
•  75% (training, 10% for validation), 25% (test)
•  Evaluation Metrics
•  precision
•  recall
•  F1-score
Experiment Setup
17	
•  Model Parameters
•  dropout rate: 0.5
•  window size: (1, 2)
•  feature maps num.: 100
•  L2 constraint: 3
•  mini-batch size: 40
•  cluster number: 5
•  word vector: word2vec trained based on Google News
•  user & author vector dimensions: 300 (the same as word embedding)
Experiment Setup
18	
•  Compared Methods
•  Random: random decision
•  Ave-SVM, Sum-SVM: average, sum of word vectors for tweet vectors
•  ASC-HDP: non-parametric statistical models [Zhang et al. AAAI’15]
•  CNN, U-CNN, UA-CNN
•  SUA-ACNN: with attention
Experimental Results
19
Experimental Results
20
Conclusions
21	
•  Proposed a novel attention-based deep neural network
•  that can perform better than state-of-the-art methods for retweet prediction
•  user, author embeddings, the similarity score and the user’s attention interests can
each significantly improve the performance
•  the integration of these components provides the best performance
22	
Guangyuan Piao
e-mail: guangyuan.piao@insight-centre.org
twitter: https://twitter.com/parklize
slideshare: http://www.slideshare.net/parklize

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Retweet Prediction with Attention-based Deep Neural Network

  • 1. Retweet Prediction with Attention- based Deep Neural Network #CIKM2016 Authors: Qi Zhang, Yeyun Gong, Jindou Wu, Haoran Huang, Xuanjing Huang Reading group: 25/10/2017 Presenter: Guangyuan Piao (Unit for Social Semantics) Mentor: Subhasis Thakur | Supervisor: John G. Breslin
  • 2. Agenda •  Background & Related Work •  Proposed Approach •  Experimental Setup & Results •  Conclusions •  Summary 2
  • 3. Background 3 •  easy real-time information sharing •  1 billion unique visits / month for Twitter
  • 5. Background – Retweeting Behavior 5 •  key mechanism for spreading information •  can help information spreading prediction, popularity prediction etc.
  • 6. (Some) Related Work 6 •  Retweeting behavior •  study of a number of features for retweetability of tweets [Suh et al., SocialCom’10] •  feature-aware factorization model [Feng et al, WSDM’13] •  considering information about user, tweet, and author •  who will retweet me? [Luo et al., SIGIR’13] •  using learning-to-rank framework •  non-parametric statistical models [Zhang et al. AAAI’15] •  combining structural, textual & temporal info.
  • 7. (Some) Related Work 7 •  Retweeting behavior •  study of a number of features for retweetability of tweets [Suh et al., SocialCom’10] •  feature-aware factorization model [Feng et al, WSDM’13] •  considering information about user, tweet, and author •  who will retweet me? [Luo et al., SIGIR’13] •  using learning-to-rank framework •  non-parametric statistical models [Zhang et al. AAAI’15] •  combining structural, textual & temporal info. feature engineering is required
  • 8. (Some) Related Work 8 •  Convolutional Neural Network (CNN) •  image recognition •  video processing •  natural language processing •  Attention-based Neural Network •  machine translation •  speech recognition •  visual object classification
  • 9. Proposed Approach – Variants of CNN approach 9 words of a tweet •  Vu: user embedding vector •  Vp: tweet embedding vector
  • 10. Proposed Approach – Variants of CNN approach 10 •  Vu: user embedding vector •  Vp: tweet embedding vector •  Va: author embedding vector
  • 11. Proposed Approach 11 •  Modeling User Interests based on Tweet History [t1, t2 … tm] •  clustering m tweets of each user into n groups using K-means •  using the central tweet of each group as an interest of user •  user interest profile [t1, t2 … tn]
  • 12. •  Modeling User Interests based on Tweet History [t1, t2 … tm] •  clustering m tweets of each user into n groups •  using the central tweet of each group as an interest of user •  user interest profile [t1, t2 … tn] •  apply CNN for each tweet to obtain tweet embeddings Proposed Approach 12
  • 13. Proposed Approach 13 •  Attention •  Folding the value in the i-th position of the embedding of the j-th attention interests
  • 15. Proposed Approach 15 •  Vu: user embedding vector •  Vi: user interest embedding vector •  S: similarity(user interest vector, tweet vector) •  Vp: tweet embedding vector •  Va: author embedding vector
  • 16. Experiment Setup 16 •  Twitter Dataset •  75% (training, 10% for validation), 25% (test) •  Evaluation Metrics •  precision •  recall •  F1-score
  • 17. Experiment Setup 17 •  Model Parameters •  dropout rate: 0.5 •  window size: (1, 2) •  feature maps num.: 100 •  L2 constraint: 3 •  mini-batch size: 40 •  cluster number: 5 •  word vector: word2vec trained based on Google News •  user & author vector dimensions: 300 (the same as word embedding)
  • 18. Experiment Setup 18 •  Compared Methods •  Random: random decision •  Ave-SVM, Sum-SVM: average, sum of word vectors for tweet vectors •  ASC-HDP: non-parametric statistical models [Zhang et al. AAAI’15] •  CNN, U-CNN, UA-CNN •  SUA-ACNN: with attention
  • 21. Conclusions 21 •  Proposed a novel attention-based deep neural network •  that can perform better than state-of-the-art methods for retweet prediction •  user, author embeddings, the similarity score and the user’s attention interests can each significantly improve the performance •  the integration of these components provides the best performance
  • 22. 22 Guangyuan Piao e-mail: guangyuan.piao@insight-centre.org twitter: https://twitter.com/parklize slideshare: http://www.slideshare.net/parklize