2018
Stai, E.; Milaiou, E.; Karyotis, V.; Papavassiliou, S.
Temporal Dynamics of Information Diffusion in Twitter: Modeling and Experimentation Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 5, no. 1, pp. 256-264, 2018, ISSN: 2329924X, (cited By 40).
Abstract | Links | BibTeX | Tags: Adaptation models; Computational model; Epidemic modeling; Hashtags; Infection rates; Information propagation; Tagging; Twitter, Analytical models; Data structures; Dynamics; Epidemiology; Information dissemination; Mathematical models, Social networking (online)
@article{Stai2018256,
title = {Temporal Dynamics of Information Diffusion in Twitter: Modeling and Experimentation},
author = {E. Stai and E. Milaiou and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040913521&doi=10.1109%2fTCSS.2017.2784184&partnerID=40&md5=7974b86c93a772693bc3d1ec0fa67f3e},
doi = {10.1109/TCSS.2017.2784184},
issn = {2329924X},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {5},
number = {1},
pages = {256-264},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type. © 2014 IEEE.},
note = {cited By 40},
keywords = {Adaptation models; Computational model; Epidemic modeling; Hashtags; Infection rates; Information propagation; Tagging; Twitter, Analytical models; Data structures; Dynamics; Epidemiology; Information dissemination; Mathematical models, Social networking (online)},
pubstate = {published},
tppubtype = {article}
}
Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type. © 2014 IEEE.