Assisted labeling for spam account detection on twitter

Risultato della ricerca: Conference contribution

Abstract

Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach.
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019
Pagine359-366
Numero di pagine8
Stato di pubblicazionePublished - 2019

Fingerprint

Labeling
Websites
Inspection
Availability
Malware

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

Cita questo

Concone, F., Morana, M., Lo Re, G., & Ruocco, C. (2019). Assisted labeling for spam account detection on twitter. In Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019 (pagg. 359-366)

Assisted labeling for spam account detection on twitter. / Concone, Federico; Morana, Marco; Lo Re, Giuseppe; Ruocco, Claudio.

Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019. 2019. pag. 359-366.

Risultato della ricerca: Conference contribution

Concone, F, Morana, M, Lo Re, G & Ruocco, C 2019, Assisted labeling for spam account detection on twitter. in Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019. pagg. 359-366.
Concone F, Morana M, Lo Re G, Ruocco C. Assisted labeling for spam account detection on twitter. In Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019. 2019. pag. 359-366
Concone, Federico ; Morana, Marco ; Lo Re, Giuseppe ; Ruocco, Claudio. / Assisted labeling for spam account detection on twitter. Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019. 2019. pagg. 359-366
@inproceedings{cbdb0f497aca480290c0a8dc82338b87,
title = "Assisted labeling for spam account detection on twitter",
abstract = "Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach.",
keywords = "Computer security, Social network, Spam detection",
author = "Federico Concone and Marco Morana and {Lo Re}, Giuseppe and Claudio Ruocco",
year = "2019",
language = "English",
isbn = "978-1-7281-1689-1",
pages = "359--366",
booktitle = "Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019",

}

TY - GEN

T1 - Assisted labeling for spam account detection on twitter

AU - Concone, Federico

AU - Morana, Marco

AU - Lo Re, Giuseppe

AU - Ruocco, Claudio

PY - 2019

Y1 - 2019

N2 - Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach.

AB - Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach.

KW - Computer security

KW - Social network

KW - Spam detection

UR - http://hdl.handle.net/10447/385997

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8777005

M3 - Conference contribution

SN - 978-1-7281-1689-1

SP - 359

EP - 366

BT - Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019

ER -