
Social Media Processing
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The 14 revised full papers and 9 short papers presented were carefully reviewed and selected from 105 submissions. The papers address issues such as: mining social media and applications; natural language processing; data mining; information retrieval; emergent social media processing problems.
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Content
- Intro
- Preface
- Organization
- Contents
- Personalized Microtopic Recommendation with Rich Information
- 1 Introduction
- 2 Microtopic Recommendation Model
- 2.1 Modeling User-Microtopic Adoptions
- 2.2 Modeling User and Microtopic Content
- 2.3 Modeling User and Microtopic Attributes
- 2.4 Complete Model and Model Inference
- 3 Experiments
- 3.1 Data Set
- 3.2 Experimental Settings
- 3.3 Collaborative Filtering with Rich Content
- 3.4 Integrating Attributes
- 4 Related Work
- 5 Conclusions
- References
- PRISM: Profession Identification in Social Media with Personal Information and Community Structure
- 1 Introduction
- 2 The Framework of PRISM
- 2.1 Profession Identification with Personal Information
- 2.2 Profession Refinement with Community Structure
- 3 Experiments and Analysis
- 3.1 Experimental Results on Profession Identification
- 4 Related Work
- 5 Conclusion
- References
- A Gaussian Copula Regression Model for Movie Box-office Revenue Prediction with Social Media
- 1 Introduction
- 2 Related Work
- 3 Copula Regression for Prediction
- 3.1 A Brief Introduction to Copula
- 3.2 Kernel Density Estimation
- 3.3 Copula Parameter Estimation
- 3.4 Inference
- 4 Experiments
- 4.1 Feature Sets
- 4.2 Baselines
- 4.3 Evaluation Metrics
- 4.4 Comparison to the Baselines
- 4.5 Varing the Amount of Training Data
- 4.6 Feature Combinations
- 5 Discussion
- 6 Conclusion
- References
- Personalized Hashtag Suggestion for Microblogs
- 1 Introduction
- 2 Related Works
- 3 Method
- 3.1 Preliminaries
- 3.2 Model Formulation
- 3.3 Learning and Inference
- 3.4 Personalized Hashtag Recommendation
- 4 Experiments and Results
- 4.1 Datasets
- 4.2 Evaluation Criteria and Experimental Setup
- 4.3 Methods for Comparison
- 4.4 Experiment Results
- 5 Conclusions
- References
- Hybrid Model Based Influenza Detection with Sentiment Analysis from Social Networks
- 1 Introduction
- 2 Em-Flu Model
- 3 Data Preparation
- 4 Experiments and Data Analysis
- 5 Conclusion
- References
- Do Photos Help Express Our Feelings: Incorporating Multimodal Features into Microblog Sentiment Analysis
- 1 Introduction
- 2 Related Work
- 3 Text Feature Extraction
- 4 Image Feature Extraction
- 4.1 Filtering and Clipping
- 4.2 Extracting Features
- 5 Experiments
- 5.1 Experiment Setups
- 5.2 Pre-experiment on Images
- 5.3 Subjectivity Sentiment Classification
- 5.4 Result Analysis
- 6 Conclusion
- References
- Nugget-Based First Story Detection in Twitter Stream
- 1 Introduction
- 2 Related Work
- 3 Nugget-Based First Story Detection
- 3.1 Task Definition
- 3.2 Nugget-Based FSD
- 3.3 Nugget Generation and Update
- 4 Experiments
- 4.1 Dataset and Evaluation
- 4.2 Results and Analysis
- 4.3 Time Analysis
- 5 Conclusion
- References
- Dirichlet Process Mixture Model for Summarizing the Social Web
- 1 Introduction
- 2 Related Work
- 3 Model
- 3.1 Chinese Restaurant Process Model
- 3.2 Algorithm
- 3.3 Application
- 4 Experiment
- 4.1 Tag-Driven Summarization
- 4.2 Comparative Summarization
- 4.3 Update Summarization
- 5 Conclusion
- References
- Approaches to Detect Micro-Blog User Interest Communities Through the Integration of Explicit User Relationship and Implicit Topic Relations
- 1 Introduction
- 2 Explicit User Relationship
- 2.1 Construct the User Follow Relationship Network
- 2.2 Construct the User Tag-Based Interest Relationship Network
- 3 Implicit Topic Relations
- 3.1 Construct the Topic-Based Interest Relationship Network
- 4 Approaches to Detect Micro-Blog User Interest Communities Through the Integration of Explicit User Relationship and Implicit Topic Relations
- 4.1 Network Convergence
- 4.2 Approaches to Detect Micro-Blog User Interest Communities
- 5 Experiments
- 5.1 Experimental Data Set
- 5.2 Evaluation Index for Community Division
- 5.3 Experimental Settings and Result Analysis
- 6 Conclusions
- References
- Supervised Link Prediction Using Random Walks
- 1 Introduction
- 2 Related Work
- 3 Supervised Random Walk with Restart
- 3.1 Generalized Bi-Relational Network
- 3.2 Our Method
- 3.3 More Alternative Features
- 4 Experiment
- 4.1 Data Set
- 4.2 Experiment Setting and Result
- 5 Conclusions
- References
- FCL: A New Network Words Extraction Approach Based on Statistical Language Knowledge
- 1 Introduction
- 2 Related Work
- 3 Our Approach
- 3.1 The Overview of Our Approach
- 3.2 A Filtering Algorithm Through the News Corpus
- 3.3 Measuring Word Features
- 3.4 A Ranking Method for New Network Words Extraction
- 4 Experiments and Discussions
- 4.1 Experiment Setup
- 4.2 Our Method vs. Different Baselines
- 4.3 Evaluation of Different Statistical Language Knowledge
- 4.4 Parameter Tuning
- 4.5 Application of New Network Words to Word Segmentation
- 5 Conclusion and Future Work
- References
- Systematic Comparison of Question Target Classification Taxonomies Towards Question Answering
- Abstract
- 1 Introduction
- 2 The Definition of Question Target Classification
- 3 Question Target Classification Taxonomy
- 4 Comparison and Analysis
- 5 Summary
- References
- Combining Convolutional Neural Network and Support Vector Machine for Sentiment Classification
- 1 Introduction
- 2 Related Work
- 2.1 Sentiment Analysis
- 2.2 Deep Neural Networks
- 3 Our Approach
- 3.1 Word Embedding Construction
- 3.2 CNN-Based Sentence Distributed Feature Representation
- 3.3 CNN-based SVMs Classifier
- 4 Evaluation and Discussion
- 4.1 Experiment Settings
- 4.2 Experimental Results and Analysis
- 5 Conclusion
- References
- A Novel Cross Modal Hashing Algorithm Based on Multi-modal Deep Learning
- 1 Introduction
- 2 Related Work
- 2.1 Cross Modal Hashing
- 2.2 Multi-modal Deep Learning
- 3 The Multi-modal Deep Learning Based Hashing Algorithm Methodology
- 3.1 Notations and Problem Definition
- 3.2 Multi-modal Deep Learning
- 3.3 Hashing Function Learning
- 4 Experiments
- 4.1 Data Sets and Settings
- 4.2 Evaluation Metric
- 4.3 Compared Methods
- 4.4 Results
- 5 Conclusion
- References
- Predicting Who Will Retweet or Not in Microblogs Network
- 1 Introduction
- 2 Features
- 2.1 Followee and Follower Features
- 2.2 Tweet Features
- 2.3 Interaction Features
- 3 Experiments
- 3.1 Data Set
- 3.2 Preprocessing
- 3.3 Predictive Models with All Features
- 3.4 Features Rank
- 3.5 Predictive Models Combining Top 12 Features
- 4 Conclusions
- References
- Predicting User Relationship from Scratch
- 1 Introduction
- 2 Related Work
- 3 Data Description
- 4 Predicting User-Relation
- 4.1 Node Features
- 4.2 Method
- 5 Experimental Results
- 6 Conclusion
- References
- Detecting Overlapping and Hierarchical Communities in Complex Network Based on Maximal Cliques
- Abstract
- 1 Introduction
- 2 Related Works
- 3 Motivation
- 3.1 Maximal Cliques
- 3.2 Extended Modularity
- 3.3 Merge
- 3.4 Algorithm Description
- 4 Experiments and Results
- 4.1 Bottlenose Dolphins Network
- 4.2 Karate Club Network
- 4.3 Email Network
- 5 Conclusion
- References
- The Expert Ranking Method Based on Listwise with Associated Features
- Abstract
- 1 Introduction
- 2 The Expert Ranking Method Based on List with Associated Features
- 2.1 The Correlation Model Based on Evidence Document
- 2.2 The Correlation Model Based on Expert Relationship Network
- 2.3 The Correlation Model Based on Expert Metadata
- 2.4 The Expert-ListNet Algorithm
- 3 The Experiment and Result Analysis
- 3.1 The Experimental Data Preparation
- 3.2 The Influence of Different Correlation Characteristics to Expert Sort
- 3.3 The Experimental Comparison of Different Methods of Expert Ranking Methods
- 4 Conclusion
- References
- Generating Triples Based on Dependency Parsing for Contradiction Detection
- 1 Introduction
- 2 Joint Method for Dp-Triple Extraction
- 2.1 Dp-Triple Extraction Method
- 2.2 Triple Alignment
- 2.3 Context of Matching Word Collection
- 3 Features for Contradiction Detection
- 4 Experiment and Analysis
- 4.1 Data Sets
- 4.2 Results and Analysis
- 5 Related Work
- 6 Conclusion
- References
- Improving Conversational Spoken Language Machine Translation via Pronoun Recovery
- 1 Introduction
- 2 Pronouns Recovery
- 2.1 Pronoun Tagging Based on Conditional Random Fields
- 2.2 Pronouns Recovery with Filtering
- 3 Experiments
- 3.1 Experimental Setup
- 3.2 Result and Discussion
- 4 Conclusions and Future Work
- References
- Analyzing the Segmentation Granularity of RTB Advertising Markets: A Computational Experiment Approach
- 1 Introduction
- 2 The Model
- 2.1 Problem Statement
- 2.2 The Model
- 3 The Experiments
- 3.1 The Computational Experiments Scenario
- 3.2 The Experimental Results
- 3.3 Analysis of the Experimental Results
- 4 Managerial Insights
- 5 Conclusions and Future Work
- References
- Combining Feature-Based and Instance-Based Transfer Learning Approaches for Cross-Domain Hedge Detection with Multiple Sources
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Cross-Domain Hedge Detection
- 3.1 Hedge Cue Detection
- 3.2 Transfer Learning with Multiple Sources
- 4 Experiments
- 4.1 Performance Based on Transfer Learning with One Source
- 4.2 Performance Based on Transfer Learning with Multiple Sources
- 5 Conclusion
- Acknowledgements
- References
- A Music Recommendation Algorithm Based on Hybrid Collaborative Filtering Technique
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Fuse the Collaborative Filtering Algorithm
- 3.1 Algorithm Overview
- 3.2 Algorithm Design
- 3.3 Weight Penalty on the Importance of Time and Item
- 4 The Experiment
- 4.1 The Experimental Data
- 4.2 Results
- 5 Conclusion
- References
- Author Index
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