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.

Exploiting digital DNA for the analysis of similarities in twitter behaviours / Cresci, Stefano; di Pietro, Roberto; Petrocchi, Marinella; Spognardi, Angelo; Tesconi, Maurizio. - (2017), pp. 686-695. (Intervento presentato al convegno International Conference on Data Science and Advanced Analytics tenutosi a tokyo) [10.1109/DSAA.2017.57].

Exploiting digital DNA for the analysis of similarities in twitter behaviours

Angelo Spognardi;
2017

Abstract

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.
2017
International Conference on Data Science and Advanced Analytics
Signal Processing; Information Systems and Management; Statistics, Probability and Uncertainty; Computer Networks and Communications
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Exploiting digital DNA for the analysis of similarities in twitter behaviours / Cresci, Stefano; di Pietro, Roberto; Petrocchi, Marinella; Spognardi, Angelo; Tesconi, Maurizio. - (2017), pp. 686-695. (Intervento presentato al convegno International Conference on Data Science and Advanced Analytics tenutosi a tokyo) [10.1109/DSAA.2017.57].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1165519
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