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Learning content–social influential features for influence analysis

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International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

We address how to measure the information propagation probability between users given certain contents. In sharp contrast to existing works that oversimplify the propagation model as predefined distributions, our approach fundamentally attempts to answer why users are influenced (e.g., by content or relations) and whether the corresponding influential features (e.g., hidden factors) can be inferred from the propagation in the entire network. In particular, we propose a novel method to deeply learn the unified feature representations for both user pair and content, where the homogeneous feature similarity can be used to estimate the propagation probability between users with given content. The features are dubbed content–social influential feature since we consider not only the content of the propagation information but also how it propagates over the social network. We design a fast asynchronous parallel algorithm for the feature learning. Through extensive experiments on a real-world social network with 53 million users and 838 million tweets, we show significantly improved performance as compared to other state-of-the-art methods on various social influence analysis tasks.

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Notes

  1. By inductively apply the fact \(\sigma (x)+\sigma (-x) = 1\).

  2. Zombies refer to soul-less accounts that post no original content, run by the shady individuals who take customers’ money in exchange for these new “fans”. Bots refer to machine generated user accounts.

  3. We did not find any significant performance variance by using 10 different random split. Therefore, we just arbitrarily chose one split.

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Acknowledgments

This work was partially supported by the NUS-Tsinghua Extreme Search (NExT) project (Grant R-252-300-001-490). NExT research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IRC@SG Funding Initiative.

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Authors and Affiliations

  1. School of Computing, National University of Singapore, 13 Computing Drive, 117417, Singapore, Singapore

    Na Zhao, Hanwang Zhang & Tat-Seng Chua

  2. School of Computer and Information, Hefei University of Technology, 198 Tunxi Road, Hefei, 230009, Anhui, China

    Meng Wang & Richang Hong

Authors
  1. Na Zhao
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  2. Hanwang Zhang
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  3. Meng Wang
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  4. Richang Hong
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  5. Tat-Seng Chua
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Correspondence to Na Zhao.

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Zhao, N., Zhang, H., Wang, M. et al. Learning content–social influential features for influence analysis. Int J Multimed Info Retr 5, 137–149 (2016). https://doi.org/10.1007/s13735-016-0102-y

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  • DOI: https://doi.org/10.1007/s13735-016-0102-y

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