Twitter spam account detection by effective labeling

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1 Citazione (Scopus)

Abstract

In the last years, the widespread diffusion of Online Social Networks (OSNs) has enabled new forms of communications that make it easier for people to interact remotely. Unfortunately, one of the first consequences of such a popularity is the increasing number of malicious users who sign-up and use OSNs for non-legit activities. In this paper we focus on spam detection, and present some preliminary results of a system that aims at speeding up the creation of a large-scale annotated dataset for spam account detection on Twitter. To this aim, two different algorithms capable of capturing the spammer behaviors, i.e., to share malicious urls and recurrent contents, are exploited. Experimental results on a dataset of about 40.000 users show the effectiveness of the proposed approach.
Lingua originaleEnglish
Numero di pagine12
Stato di pubblicazionePublished - 2019

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Communication

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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title = "Twitter spam account detection by effective labeling",
abstract = "In the last years, the widespread diffusion of Online Social Networks (OSNs) has enabled new forms of communications that make it easier for people to interact remotely. Unfortunately, one of the first consequences of such a popularity is the increasing number of malicious users who sign-up and use OSNs for non-legit activities. In this paper we focus on spam detection, and present some preliminary results of a system that aims at speeding up the creation of a large-scale annotated dataset for spam account detection on Twitter. To this aim, two different algorithms capable of capturing the spammer behaviors, i.e., to share malicious urls and recurrent contents, are exploited. Experimental results on a dataset of about 40.000 users show the effectiveness of the proposed approach.",
keywords = "Social Network Security · Spam Detection · Twitter Data Analysis",
author = "Federico Concone and Marco Morana and {Lo Re}, Giuseppe and Claudio Ruocco",
year = "2019",
language = "English",

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T1 - Twitter spam account detection by effective labeling

AU - Concone, Federico

AU - Morana, Marco

AU - Lo Re, Giuseppe

AU - Ruocco, Claudio

PY - 2019

Y1 - 2019

N2 - In the last years, the widespread diffusion of Online Social Networks (OSNs) has enabled new forms of communications that make it easier for people to interact remotely. Unfortunately, one of the first consequences of such a popularity is the increasing number of malicious users who sign-up and use OSNs for non-legit activities. In this paper we focus on spam detection, and present some preliminary results of a system that aims at speeding up the creation of a large-scale annotated dataset for spam account detection on Twitter. To this aim, two different algorithms capable of capturing the spammer behaviors, i.e., to share malicious urls and recurrent contents, are exploited. Experimental results on a dataset of about 40.000 users show the effectiveness of the proposed approach.

AB - In the last years, the widespread diffusion of Online Social Networks (OSNs) has enabled new forms of communications that make it easier for people to interact remotely. Unfortunately, one of the first consequences of such a popularity is the increasing number of malicious users who sign-up and use OSNs for non-legit activities. In this paper we focus on spam detection, and present some preliminary results of a system that aims at speeding up the creation of a large-scale annotated dataset for spam account detection on Twitter. To this aim, two different algorithms capable of capturing the spammer behaviors, i.e., to share malicious urls and recurrent contents, are exploited. Experimental results on a dataset of about 40.000 users show the effectiveness of the proposed approach.

KW - Social Network Security · Spam Detection · Twitter Data Analysis

UR - http://hdl.handle.net/10447/344306

M3 - Other

ER -