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Measuring the effects of repeated and diversified influence mechanism for information adoption on Twitter

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Abstract

People can adopt information disseminated in online social networks whenever they receive it frequently from friends or others. Studying this social influence dynamic is crucial to understanding social interactions and users’ behavior regarding online information spread. Quantifying social influence is challenging in online social systems where the interactions and communication content can be closely followed. Here, we study the effects of repeated and diversified influence mechanisms exploring the concepts of Information susceptibility and Adoption thresholds of Twitter users. We consider hashtag and retweet adoptions on different aggregation levels: items, users, and topic groups and study the adoption characterized by diversified and repeated influence stimuli. We address this challenge by tracking the timeline order of potential influence and adopting hashtags and retweets in a specific dataset collected from Twitter, which contains the posts’ dynamics of thousands of seed users and their entire followee networks. We show that users adopt retweets easier than hashtags, and we find both metrics to be heterogeneously distributed, correlated, and dependent on the topics and aggregation level of social influence. We find that new influencing neighbors can effectively trigger adoptions, particularly for topics where a new adopter friend triggers ~ 50% of adoptions. Our results may inform better models of adoption processes leading to a deeper empirical understanding of simple and complex contagion in online social networks.

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Notes

  1. The maximum number of collected tweets for a user is 3200, which covers their timeline to the past for a varying length depending on their tweeting activity. However, even the most active users may post less than this number over a year, thus setting a cap on this period assures the collection of all tweets for most user over 13 months.

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Acknowledgements

This work was supported by CAPES, FAPEMIG (PPM-00253-18), and the STIC-AmSud Program (Project 18-STIC-07). MK was supported by the DataRedux (ANR-19-CE46-0008) and SoSweet (ANR-15-CE38-0011) ANR projects and the SoBigData++ (H2020-871042) project.

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Authors and Affiliations

  1. Department of Computer Science, PUC Minas, Belo Horizonte, Brazil

    Jaqueline Faria de Oliveira & Humberto Torres Marques-Neto

  2. Department of Network and Data Science, Central European University, Vienna, Austria

    Márton Karsai

  3. ENS de Lyon, Inria, CNRS, UCB Lyon 1, LIP UMR 5668, IXXI, Univ Lyon, 69342, Lyon, France

    Jaqueline Faria de Oliveira & Márton Karsai

Authors
  1. Jaqueline Faria de Oliveira
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  2. Humberto Torres Marques-Neto
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  3. Márton Karsai
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Correspondence to Jaqueline Faria de Oliveira.

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de Oliveira, J.F., Marques-Neto, H.T. & Karsai, M. Measuring the effects of repeated and diversified influence mechanism for information adoption on Twitter. Soc. Netw. Anal. Min. 12, 16 (2022). https://doi.org/10.1007/s13278-021-00844-x

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