Presentation on theme: "Self-introduction Name: 鲍鹏 (Peng Bao) Research Interests: Popularity Prediction, Information Diffusion, Social Network ， etc… Grade: In the third."— Presentation transcript:
Self-introduction Name: 鲍鹏 (Peng Bao) Research Interests: Popularity Prediction, Information Diffusion, Social Network ， etc… Grade: In the third year pursuing for the PhD. Group: NASC(Network Analysis and Social Computing) Lab: Research Center of Web Data Science & Engineering Doctoral supervisor: Prof. Xue-Qi Cheng
popularity prediction in Microblogging Network ——An empirical study Authors: Peng Bao, Hua-Wei Shen, Junming Huang, Xue-Qi Cheng Previous Work
Outline Background & Motivation Problem definition Related works Preliminary study Structural characteristics Prediction & Results Conclusions and Discussions
Background BURST of SNS Everyone is a member in the We the Media age! Sina Weibo plays an more and more important social role. Opportunities and Challenges Special issues in Sci./Nat. Computational social science [D. Lazer et al. Science 323, (2009)]
Challenging An interesting and fundamental question How to track, to understand, and to predict the information flow on the network? To predict the long-term popularity of online content is very HARD! Popularity is unequally distributed. high interaction among users intrinsic interestingness of content external influence from traditional media active period of users
Motivation Popularity prediction is USEFUL! From technology view Drive enterprises to design a cost-effective cache and content distribution mechanism system From business view Help journalists, content providers, advertisers, news recommend systems to provide information services and to design viral marketing strategy From sociology view Reveal the human collective behavior Facilitate governors to supervise and to guide public opinion Increasing availability of data increase Predictability! Increasing availability of data increase Predictability!
Problem definition Popularity prediction: Given a tweet and its forward information before an indicating time t i, We want to predict the popularity p(t r ) at a reference time t r. Indicating time t i : The time at which we observe the information of a tweet. Reference time t r : The time at which we intend to predict the popularity of a tweet. Popularity p(t) : The number of times that a tweet is re-tweeted at time t.
Related works Temporal correlation based [SzaBo et al. C ACM 2010] Strong correlation between Early and later log popularity Linear regression Visibility and Interestingness based [Lerman et al. WWW 2010] User behavior modeling Estimate the interestingness
Related works cont.’ Matrix Factorization based [Cui et al. SIGIR 2011] Estimate the latent factor of user and item Feature based [Hong et al. WWW 2011] Formalized to classification problem Logistic regression Temporal pattern based [Matsubara et al. KDD 2012] Periodical Avoid infinity Power-law decay Existing methods mainly focused on the quality of content, the interface of the social media site, the collective behavior of users. We focus on the structural characteristics of the networks spanned by early adopters We focus on the structural characteristics of the networks spanned by early adopters
Preliminary study Popularity distribution The popularity of tweets roughly follows a power-law distribution, distributes very unequally. The popularity of tweets roughly follows a power-law distribution, distributes very unequally.
Preliminary study Lifespan of tweets Most tweets receive 80% of the final popularity in 24 hours and 90% in 48 hours. The lifespan of tweets follows a log-normal distribution. Most tweets receive 80% of the final popularity in 24 hours and 90% in 48 hours. The lifespan of tweets follows a log-normal distribution.
Preliminary study Active period We should consider the variation in hourly activity cycles The daily variation has no obvious relationship with week cycle and are event-related. We should consider the variation in hourly activity cycles The daily variation has no obvious relationship with week cycle and are event-related. “Wenzhou train collision”
Temporal correlation of logarithmic popularity The correlation is weak with large deviation. The Pearson Correlation Coefficients is 0.74 It is less reliable to predict the popularity of a tweet if we just use its earlier popularity alone. The correlation is weak with large deviation. The Pearson Correlation Coefficients is 0.74 It is less reliable to predict the popularity of a tweet if we just use its earlier popularity alone.
Structural characteristics We explore the network consisting of early adopters Link density: the ratio of the number of existing follow- ship links and the number of all possible links. Diffusion depth: the length of the longest path from the submitter to anyone of them.
Structural characteristics Empirical found The structural characteristics provide strong evidence to help estimate the final popularity The structural characteristics provide strong evidence to help estimate the final popularity
Prediction and Results Comparison approaches: Evaluation methods: Experiment results
Conclusions We empirically study structural characteristics, which can provide critical indicators The prediction accuracy can be significantly improved by incorporating the factor of structural diversity The conclusion capture the intuition It provides us INSIGHTS to further study
Accumulative Effect of Multiple Exposure in Information Diffusion on Social Network On-Going Work
Exposures and Adoptions Exposures: Node’s neighbor exposes the node to the contagion Adoption: The node acts (e.g. re-tweet) on the contagion t3t3 t1t1 t2t2 Time: t 1 < t 2 < t 3 < … < t n
Problem definition Exposure Curve: Probability of re-tweeting a tweet for a user depends on the number of friends who have already re-tweeted. Dependence
Example Application Marketing agency: would like you to adopt/buy product X They estimate the adoption curve Should they expose you to X three times? Or, is it better to expose you X, then Y and then X again?
What we are doing Classify the TWEETS by Has URL or not Has Event or not Has Multiple Events or not Deeper analysis on the ME for different event Classify the USERS by User’s degree User’s active period Local clustering coefficient
What we are doing cont.’ Structural diversity between the source of multiple exposures Fix the number of exposure times, check Link density Number of connected components Temporal effect Temporal motif You will see the results soon!
Closing Remarks We should do MORE… We knew A LITTLE. We have done MANY! This field is a piece of WILD but Fertile mineral land.
Acknowledgement Thank to all members in the NASC group (www.groupnasc.org) for helpful discussions and suggestionswww.groupnasc.org Collaborators Xue-Qi Cheng,Hua-Wei Shen,Junming Huang