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Faderank: An Incremental Algorithm for Ranking Twitter Users

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Web Information Systems Engineering – WISE 2016 (WISE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10042))

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Abstract

User reputation is a crucial indicator in social networks, where it is exploited to promote authoritative content and to marginalize spammers. To be accurate, reputation must be updated periodically, taking into account the whole historical data of user activity. In big social networks like Twitter and Facebook, these updates would require to process a huge amount of historical data, and therefore pose serious performance issues. We address these issues in the context of Twitter, by studying a technique which can update user reputation in constant time. This is obtained by using an arbitrary ranking algorithm to compute user reputation in the most recent time window, and by combining it with a summary of historical data. Experimental evaluation on large datasets show that our technique improves the performance of existing ranking algorithms, at the cost of a negligible degradation of their precision.

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Notes

  1. 1.

    Note that we cannot use PageRank as is because of limitations of Twitter APIs, which do not allow to obtain temporal information about the “follow” relation. To circumvent this limitation, our variant of PageRank operates on the “tweet” and “retweet” relations, by assigning to each user the sum of the score of its tweets.

  2. 2.

    Note that we cannot compute the \(\gamma \)-weights by solving the linear system, because the value \(D_t^n\) is a linear combination of the other two (i.e., \(D_t^n = R_t^n - H_t^n\)).

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Acknowledgments

This work has been partially supported by Aut. Reg. of Sardinia P.I.A. 2013 “NOMAD”, and by EU COST Action IC1201 “Behavioural Types for Reliable Large-Scale Software Systems” (BETTY).

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Authors and Affiliations

  1. Università degli Studi di Cagliari, Cagliari, Italy

    Massimo Bartoletti, Stefano Lande & Alessandro Massa

  2. Xorovo.com, Cagliari, Italy

    Alessandro Massa

Authors
  1. Massimo Bartoletti
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  2. Stefano Lande
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  3. Alessandro Massa
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Corresponding author

Correspondence to Massimo Bartoletti .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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Bartoletti, M., Lande, S., Massa, A. (2016). Faderank: An Incremental Algorithm for Ranking Twitter Users. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-48743-4_5

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  • Online ISBN: 978-3-319-48743-4

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