Exploiting digital DNA for the analysis of similarities in twitter behaviours

Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages686-695
Number of pages10
ISBN (Print)9781509050048
DOIs
StatePublished - Jul 2 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20

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