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Predicting Retweet Behavior in Online Social Networks Based on Locally Available Information

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10047))

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Abstract

Behavior prediction in online social networks (OSNs) has attracted lots of attention due to its vast applications. However, most previous work needs global network information to train classifiers. Due to the large data volume and privacy concern, it is infeasible to obtain global network information for every OSN. We propose a decentralized framework, named REPULSE, to predict whether a target user will retweet a message relayed by his friends. We also identify a new set of community-related features that improve retweet prediction accuracy considerably.

To demonstrate the value of community-related features, we propose another framework named HOTPIE to predict tweets popularity. Utilizing community-related features can boost the F1 score of popularity prediction from 0.43 to 0.55. To the best of our knowledge, this is the first work which systematically studies the impact of global vs. locally observable information on the prediction of retweet behavior in OSNs.

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Notes

  1. 1.

    In Twitter, only a complete set of users who have retweeted the same message is shown, without disclosing the actual ordering. This set of forwarding users in Twitter aggregates information from different retweet-paths in the overall diffusion graph. Note that the set of forwarding users can serve the same purpose as retweet-paths do.

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Author information

Authors and Affiliations

  1. The Chinese University of Hong Kong, Shatin, Hong Kong

    Guanchen Li & Wing Cheong Lau

Authors
  1. Guanchen Li
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  2. Wing Cheong Lau
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Corresponding author

Correspondence to Guanchen Li .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

Appendices

A Workflow of HOTPIE

Fig. 6.
figure 6

Workflow of HOTPIE

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B Confusion Matrices of HOTPIE and PPuG

Table 4. Confusion matrix of using HOTPIE, with per class accuracy
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Table 5. Confusion matrix of using PPuG, with per class accuracy
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Table 6. Confusion matrix without community-related features, with per class accuracy
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C Full Feature List

Table 7. Feature names with feature IDs
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Li, G., Lau, W.C. (2016). Predicting Retweet Behavior in Online Social Networks Based on Locally Available Information. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-47874-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47873-9

  • Online ISBN: 978-3-319-47874-6

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