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Exploiting digital DNA for the analysis of similarities in Twitter behaviours

Recently, DNA-inspired online behavioral modeling and analysis techniques have been proposed and successfully applied to a broad range of tasks. In this paper, we employ a DNA-inspired technique to investigate the fundamental laws that drive the occurrence of similarities among Twitter users. The achieved results are multifold. First, we demonstrate that, despite apparently showing little to no similarities, the online behaviors of Twitter users are far from being uniformly random. Then, we perform a set of simulations to benchmark different behavioral models and to identify the models that better resemble human behaviors in Twitter. Finally, we demonstrate that the number and the extent of behavioral similarities within a group of Twitter users obey a log-normal distribution. Our results shed light on the fundamental properties that drive behaviors of groups of Twitter users, through the lenses of DNA-inspired behavioral modeling techniques. Our datasets are publicly available to the scientific community to further explore analytics of online behaviors.
The 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA'17), Tokyo, Giappone, 2017

Autori esterni: Roberto Di Pietro (Nokia Bell Labs), Angelo Spognardi (Sapienza Università di Roma)
Autori IIT:

Tipo: Contributo in atti di convegno
Area di disciplina: Computer Science & Engineering

File: Cresci, 2017, Exploiting digital DNA for the analysis of similarities in Twitter behaviours.pdf

Attività: Social Media Analysis