TY - JOUR
T1 - Measuring the effects of repeated and diversified influence mechanism for information adoption on Twitter
AU - de Oliveira, Jaqueline Faria
AU - Marques-Neto, Humberto Torres
AU - Karsai, Márton
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Adoption
KW - Social contagion
KW - Susceptibility
KW - Threshold
UR - http://www.scopus.com/inward/record.url?scp=85121005241&partnerID=8YFLogxK
U2 - 10.1007/s13278-021-00844-x
DO - 10.1007/s13278-021-00844-x
M3 - Review Article
AN - SCOPUS:85121005241
SN - 1869-5450
VL - 12
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 16
ER -