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STH-Bass: A Spatial-Temporal Heterogeneous Bass Model to Predict Single-Tweet Popularity

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9643))

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Abstract

Prediction in social networks attracts more and more attentions since social networks have become an important part of people’s lives. Although a few topic or event prediction models have been proposed in the past few years, researches that focus on the single tweet prediction just emerge recently. In this paper, we propose STH-Bass, a Spatial and Temporal Heterogeneous Bass model derived from economic field, to predict the popularity of a single tweet. Leveraging only the first day’s information after a tweet is posted, STH-Bass can not only predict the trend of a tweet with favorite count and retweet count, but also classify whether the tweet will be popular in the future. We perform extensive experiments to evaluate the efficiency and accuracy of STH-Bass based on real-world Twitter data. The evaluation results show that STH-Bass obtains much less APE than the baselines when predicting the trend of a single tweet, and an average of 24 % higher precision when classifying the tweets popularity.

This work has been supported in part by the China 973 project (2012CB316200), National Natural Science Foundation of China (Grant number 61202024, 61472252, 61133006, 61272443, 61473109), the Opening Project of Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security) Grant number C15602, and the Opening Project of Baidu (Grant number 181515P005267).

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Notes

  1. 1.

    https://dev.twitter.com/.

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Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yan Yan, Zhaowei Tan, Xiaofeng Gao & Guihai Chen

  2. Department of Information Systems, The University of Texas at Dallas, Richardson, USA

    Shaojie Tang

Authors
  1. Yan Yan
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  2. Zhaowei Tan
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  3. Xiaofeng Gao
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  4. Shaojie Tang
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  5. Guihai Chen
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Corresponding author

Correspondence to Xiaofeng Gao .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas , Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota , Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University , Beijing, China

    Xiaoyong Du

  5. Fudan University , Shanghai, China

    Sean X. Wang

  6. Rutgers, The State University of New Jer , New Brunswick, New Jersey, USA

    Hui Xiong

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© 2016 Springer International Publishing Switzerland

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Yan, Y., Tan, Z., Gao, X., Tang, S., Chen, G. (2016). STH-Bass: A Spatial-Temporal Heterogeneous Bass Model to Predict Single-Tweet Popularity. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-32049-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32048-9

  • Online ISBN: 978-3-319-32049-6

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