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Prediction of Re-tweeting Activities in Social Networks Based on Event Popularity and User Connectivity

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 10935)

Abstract

This paper proposes an approach to predict the volume of future re-tweets for a given original short message (tweet). In our research we adopt a probabilistic collaborative filtering prediction model called Matchbox in order to predict the number of re-tweets based on event popularity and user connectivity. We have evaluated our approach on a real-world dataset and we furthermore compare our results to two baselines. We use the datasets crawled by the WISE 2012 Challenge (http://www.wise2012.cs.ucy.ac.cy/challenge.html) from Sina Weibo (http://weibo.com), which is a popular Chinese microblogging site similar to Twitter. Our experiments show that the proposed approach can effectively predict the amount of future re-tweets for a given original short message.

Keywords

  • Re-tweets
  • Prediction
  • Micro-blog
  • Social networks

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  • DOI: 10.1007/978-3-319-96133-0_27
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Notes

  1. 1.

    https://wikipedia.org.

  2. 2.

    https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-matchbox-recommender.

  3. 3.

    http://content.wuala.com/contents/imc_ecnu/wise_challenge/A4_T2GTruth.zip?dl=1.

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Correspondence to Sayan Unankard .

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Unankard, S. (2018). Prediction of Re-tweeting Activities in Social Networks Based on Event Popularity and User Connectivity. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_27

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  • DOI: https://doi.org/10.1007/978-3-319-96133-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96132-3

  • Online ISBN: 978-3-319-96133-0

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