Factors that influence content popularity

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Table of contents

1 Introduction 
1.1 Context and motivation
1.2 Global scenario and research challenges
1.3 Contributions of this thesis
1.3.1 A survey on predicting the popularity of web content
1.3.2 Predicting the popularity of online news
1.3.3 Proactive seeding based on content popularity prediction
1.3.4 Predicting κ-contact opportunities between mobile users
1.4 Thesis outline
2 A survey on predicting the popularity of web content 
2.1 Introduction
2.2 Domains
2.3 Performance measures
2.3.1 Numeric prediction
2.3.2 Classification
2.4 A classification of web content popularity prediction methods
2.4.1 Single domain
2.4.1.1 Before publication
2.4.1.2 After publication
2.4.2 Cross domain
2.5 A survey on popularity prediction methods
2.5.1 Single domain
2.5.1.1 Before publication
2.5.1.2 After publication – Aggregate behavior
2.5.1.3 After publication – Individual behavior
2.5.2 Cross domain
2.6 Selecting the right features
2.7 Factors that influence content popularity
2.8 Predictive proactive seeding: an application of web content popularity prediction
2.9 Conclusions
3 Predicting the popularity of online news articles 
3.1 Introduction
3.2 Background
3.3 Global statistics
3.3.1 Online news data collections
3.3.2 News articles lifetime
3.3.3 Distribution of popularity
3.4 Predicting the popularity of online news articles
3.4.1 Popularity predictions methods
3.4.2 Popularity prediction accuracy
3.5 Ranking news articles based on popularity prediction
3.5.1 Methodology
3.5.2 Ranking methods
3.5.3 Ranking performance
3.5.4 An alternative to learning to rank algorithms
3.6 Conclusions
4 Predictive proactive seeding for mobile opportunistic data offloading 
4.1 Introduction
4.2 Background
4.3 Global scenario
4.4 Proactive seeding in mobile opportunistic networks
4.4.1 Premise for effective proactive seeding
4.4.2 Proactive seeding strategies
4.5 Evaluation
4.5.1 Simulating user behavior
4.5.2 Simulation scenario
4.5.3 Results
4.6 Conclusion
5 Beyond contact predictions in mobile opportunistic networks 
5.1 Introduction
5.2 Background
5.3 Vicinity and data sets
5.3.1 Beyond contact relationships
5.3.2 κ-vicinity, κ-contact, and κ-intercontact
5.3.3 Data sets
5.4 Pairwise relationships under the κ-contact case
5.4.1 Pairwise minimum distance
5.4.2 Analyzing the distribution of pairwise distance
5.4.3 The stability of κ-contact relationships
5.5 Predicting κ-contact encounters
5.5.1 Dynamic graph representation
5.5.2 κ-contact prediction problem
5.5.3 The effect of time-window duration and past data
5.5.4 κ-contact prediction results
5.6 Practical implications
5.7 Conclusions
6 Conclusions and future work 
6.1 Summary
6.2 Looking ahead
6.2.1 Improving the quality of the prediction
6.2.2 Smart proactive seeding
6.2.3 Predicting spatiotemporal contacts
6.2.4 Mobile opportunistic data offloading engine
List of Publications 
References