%0 Journal Article %1 Gleeson2016Effects %A Gleeson, James P. %A O'Sullivan, Kevin P. %A Ba\ nos, Raquel A. %A Moreno, Yamir %D 2016 %J Physical Review X %K twitter social-networks predictive-models popularity memory %N 2 %R 10.1103/PhysRevX.6.021019 %T Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena %U http://dx.doi.org/10.1103/PhysRevX.6.021019 %V 6 %X Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and which can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena which, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

@article{Gleeson2016Effects, abstract = {{Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and which can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena which, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.}}, added-at = {2019-06-10T14:53:09.000+0200}, archiveprefix = {arXiv}, author = {Gleeson, James P. and O'Sullivan, Kevin P. and Ba\ {n}os, Raquel A. and Moreno, Yamir}, biburl = {https://www.bibsonomy.org/bibtex/2357381b42c531a1fff132c54334b84d2/nonancourt}, citeulike-article-id = {13504237}, citeulike-linkout-0 = {http://dx.doi.org/10.1103/PhysRevX.6.021019}, citeulike-linkout-1 = {http://arxiv.org/abs/1501.05956}, citeulike-linkout-2 = {http://arxiv.org/pdf/1501.05956}, day = 13, doi = {10.1103/PhysRevX.6.021019}, eprint = {1501.05956}, interhash = {760db66f94fb2e29c5e90ef680944fbc}, intrahash = {357381b42c531a1fff132c54334b84d2}, issn = {2160-3308}, journal = {Physical Review X}, keywords = {twitter social-networks predictive-models popularity memory}, month = may, number = 2, posted-at = {2016-02-25 15:07:20}, priority = {2}, timestamp = {2019-08-22T16:29:17.000+0200}, title = {{Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena}}, url = {http://dx.doi.org/10.1103/PhysRevX.6.021019}, volume = 6, year = 2016 }