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

Jaqueline Faria de Oliveira, Humberto Torres Marques-Neto, Márton Karsai

Research output: Contribution to journalReview Articlepeer-review

Abstract (may include machine translation)

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.

Original languageEnglish
Article number16
JournalSocial Network Analysis and Mining
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Keywords

  • Adoption
  • Social contagion
  • Susceptibility
  • Threshold

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