DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

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

doi:10.1109/MIS.2016.29

DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

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

University of Pisa
University of Padua
National Research Council of Italy

Research output: Contribution to journal › Journal article › Research › peer-review

421 Downloads (Orbit)

Abstract

A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. Then, standard DNA analysis techniques discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports the proposal, showing its effectiveness and viability. Although Twitter spambot detection is a specific use case on a specific social media platform, the proposed methodology is platform and technology agnostic, paving the way for diverse behavioral characterization tasks.

Original language	English
Journal	I E E E Intelligent Systems
Volume	31
Issue number	5
Pages (from-to)	58-64
ISSN	1541-1672
DOIs	https://doi.org/10.1109/MIS.2016.29
Publication status	Published - 2016

Access to Document

10.1109/MIS.2016.29

HKKR 1602Accepted author manuscript, 366 KB

OpenUrl availability

Check availability in DTU Findit

Cite this

@article{8c259e6440214ff9b5fe701665839cfe,

title = "DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection",

abstract = "A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. Then, standard DNA analysis techniques discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports the proposal, showing its effectiveness and viability. Although Twitter spambot detection is a specific use case on a specific social media platform, the proposed methodology is platform and technology agnostic, paving the way for diverse behavioral characterization tasks.",

author = "Stefano Cresci and \{Di Pietro\}, Roberto and Marinella Petrocchi and Angelo Spognardi and Maurizio Tesconi",

year = "2016",

doi = "10.1109/MIS.2016.29",

language = "English",

volume = "31",

pages = "58--64",

journal = "I E E E Intelligent Systems",

issn = "1541-1672",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "5",

}

TY - JOUR

T1 - DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

AU - Cresci, Stefano

AU - Di Pietro, Roberto

AU - Petrocchi, Marinella

AU - Spognardi, Angelo

AU - Tesconi, Maurizio

PY - 2016

Y1 - 2016

N2 - A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. Then, standard DNA analysis techniques discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports the proposal, showing its effectiveness and viability. Although Twitter spambot detection is a specific use case on a specific social media platform, the proposed methodology is platform and technology agnostic, paving the way for diverse behavioral characterization tasks.

AB - A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. Then, standard DNA analysis techniques discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports the proposal, showing its effectiveness and viability. Although Twitter spambot detection is a specific use case on a specific social media platform, the proposed methodology is platform and technology agnostic, paving the way for diverse behavioral characterization tasks.

U2 - 10.1109/MIS.2016.29

DO - 10.1109/MIS.2016.29

M3 - Journal article

SN - 1541-1672

VL - 31

SP - 58

EP - 64

JO - I E E E Intelligent Systems

JF - I E E E Intelligent Systems

IS - 5

ER -

DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

Abstract

Access to Document

OpenUrl availability

Fingerprint

Cite this