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An action–reaction influence model relying on OSN user-generated content

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Abstract

Due to the sustained popularization of Online Social Networks (OSNs), it has become of interest for a variety of domains of applications to correctly characterize how the behavior of an individual user can be influenced by the actions of other users in a network. Additionally, the richness of available features in modern OSNs highlights the growing importance of user-generated data in establishing user relations. In this paper, we follow a data-driven methodology and propose a diffusion algorithm designed around user-to-content relationships and an action–reaction paradigm. Crucially, we design our approach by integrating different cross-disciplinary theories of how users influence each other. Thus, we enrich the influence maximization task with a psychological dimension and define a model that ties influence diffusion to recurrent users’ behavior from OSN logs, considering relationships between users mediated by user-generated content. We evaluate our approach over the Yahoo Flickr Creative Commons 100 Million real-world dataset. We measure efficiency and effectiveness by analyzing scalability and spread efficacy and show how our model outperforms existing state-of-the-art methods.

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Notes

  1. https://www.flickr.com/.

  2. https://azure.microsoft.com/.

  3. https://azure.microsoft.com/it-it/services/hdinsight/.

  4. https://www.tweepy.org/.

  5. https://www.yelp.com/developers.

  6. https://developers.facebook.com/.

  7. https://www.flickr.com/services/api/.

  8. https://cassandra.apache.org/.

  9. https://spark.apache.org/.

  10. https://github.com/yahoo/TensorFlowOnSpark.

  11. https://www.instagram.com/.

  12. https://www.tiktok.com/.

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Authors and Affiliations

  1. Department of Linguistics, University of Utah, Salt Lake City, UT, 84112, USA

    Aniello De Santo

  2. Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Antonino Ferraro, Vincenzo Moscato & Giancarlo Sperlí

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  1. Aniello De Santo
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  2. Antonino Ferraro
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  3. Vincenzo Moscato
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  4. Giancarlo Sperlí
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ADS: conception and design of study, acquisition of data, analysis and/or interpretation of data, Writing—original draft, Writing—review and editing. AF: conception and design of study, acquisition of data, analysis and/or interpretation of data, writing—original draft, writing—review and editing. VM: conception and design of study, acquisition of data, analysis and/or interpretation of data, writing—original draft, writing—review and editing. GS: conception and design of study, acquisition of data, analysis and/or interpretation of data, writing—original draft, writing—review and editing.

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Correspondence to Giancarlo Sperlí.

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De Santo, A., Ferraro, A., Moscato, V. et al. An action–reaction influence model relying on OSN user-generated content. Knowl Inf Syst 65, 2251–2280 (2023). https://doi.org/10.1007/s10115-023-01833-6

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