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Defining and evaluating Twitter influence metrics: a higher-order approach in Neo4j

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Abstract

Ranking account influence constitutes an important challenge in social media analysis. Until recently, influence ranking relied solely on the structural properties of the underlying social graph, in particular on connectivity patterns. Currently, there has been a notable shift to the next logical step where network functionality is taken into account, as online social media such as Reddit, Instagram, and Twitter are renowned primarily for their functionality. However, contrary to structural rankings, functional ones are bound to be network-specific since each social platform offers unique interaction possibilities. This article examines seven first-order influence metrics for Twitter, defines a strategy for deriving their higher-order counterparts, and outlines a probabilistic evaluation framework. Experiments with a Twitter subgraph with ground truth influential accounts indicate that a single metric combining structural and functional features outperforms the rest in said framework.

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Notes

  1. https://networkx.github.io.

  2. http://spark.apache.org/graphx.

  3. http://www.influencetracker.com.

  4. http://www.tweepy.org.

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Authors and Affiliations

  1. Department of Informatics, Ionian University, 49100, Corfu, Greece

    Georgios Drakopoulos, Phivos Mylonas & Spyros Sioutas

  2. Computer Engineering and Informatics Department, University of Patras, 26504, Patras, Greece

    Andreas Kanavos

Authors
  1. Georgios Drakopoulos
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  2. Andreas Kanavos
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  3. Phivos Mylonas
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  4. Spyros Sioutas
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Correspondence to Andreas Kanavos.

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Drakopoulos, G., Kanavos, A., Mylonas, P. et al. Defining and evaluating Twitter influence metrics: a higher-order approach in Neo4j. Soc. Netw. Anal. Min. 7, 52 (2017). https://doi.org/10.1007/s13278-017-0467-9

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