A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading.
Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki(American Physical Society, 2017)
Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically ...
O'Sullivan, David J.P.; Garduño-Hernández, Guillermo; Gleeson, James P.; Beguerisse-Díaz, Mariano(The Royal Society, 2017)
We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the
sentiment of every tweet with the hashtags ...
O'Sullivan, David J.P.; O'Keeffe, Gary J.; Fennell, Peter G.; Gleeson, James P.(Frontiers Media, 2015)
The spreading of behavior, such as the adoption of a new innovation, is influenced by the structure of social networks that interconnect the population. In the experiments of Centola [15], adoption of new behavior was shown ...
O'Brien, Joseph D.; Gleeson, James P.; O'Sullivan, David J.P.(Public Library of Science, 2021)
In all competitions where results are based upon an individual’s performance the question of whether the outcome is a consequence of skill or luck arises. We explore this question
through an analysis of a large dataset ...
Gleeson, James P.; Murphy, Thomas Brendan; O'Brien, Joseph D.; Friel, Nial; Bargary, Norma; O'Sullivan, David J.P.(The Royal Society Publishing, 2021)
We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model ...
