research-article

Short links under attack: geographical analysis of spam in a URL shortener network

Authors:

Florian Klien,

Markus StrohmaierAuthors Info & Claims

HT '12: Proceedings of the 23rd ACM conference on Hypertext and social media

Pages 83 - 88

https://doi.org/10.1145/2309996.2310010

Published: 25 June 2012 Publication History

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Abstract

URL shortener services today have come to play an important role in our social media landscape. They direct user attention and disseminate information in online social media such as Twitter or Facebook. Shortener services typically provide short URLs in exchange for long URLs. These short URLs can then be shared and diffused by users via online social media, e-mail or other forms of electronic communication. When another user clicks on the shortened URL, she will be redirected to the underlying long URL. Shortened URLs can serve many legitimate purposes, such as click tracking, but can also serve illicit behavior such as fraud, deceit and spam. Although usage of URL shortener services today is ubiquituous, our research community knows little about how exactly these services are used and what purposes they serve. In this paper, we study usage logs of a URL shortener service that has been operated by our group for more than a year. We expose the extent of spamming taking place in our logs, and provide first insights into the planetary-scale of this problem. Our results are relevant for researchers and engineers interested in understanding the emerging phenomenon and dangers of spamming via URL shortener services.

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Cited By

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Sahoo SGupta BPeraković DPeñalvo FCvitić I(2022)Spammer Detection Approaches in Online Social Network (OSNs): A SurveySustainable Management of Manufacturing Systems in Industry 4.010.1007/978-3-030-90462-3_11(159-180)Online publication date: 1-Feb-2022
https://doi.org/10.1007/978-3-030-90462-3_11
Indrasiri PHalgamuge MMohammad A(2021)Robust Ensemble Machine Learning Model for Filtering Phishing URLs: Expandable Random Gradient Stacked Voting Classifier (ERG-SVC)IEEE Access10.1109/ACCESS.2021.31246289(150142-150161)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124628
Hai-Jew S(2020)Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL BasicRobotic Systems10.4018/978-1-7998-1754-3.ch060(1250-1289)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1754-3.ch060
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Index Terms

Short links under attack: geographical analysis of spam in a URL shortener network
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information systems applications
    1. Digital libraries and archives
  2. World Wide Web
    1. Web applications
    2. Web services

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Information & Contributors

Information

Published In

HT '12: Proceedings of the 23rd ACM conference on Hypertext and social media

June 2012

340 pages

ISBN:9781450313353

DOI:10.1145/2309996

General Chair:
Ethan Munson
University of Wisconsin - Milwaukee, USA
,
Program Chair:
Markus Strohmaier
Graz University of Technology, Austria

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2012

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SIGWEB

HT '12: 23rd ACM Conference on Hypertext and Social Media

June 25 - 28, 2012

Wisconsin, Milwaukee, USA

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HT '12 Paper Acceptance Rate 33 of 120 submissions, 28%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

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Sahoo SGupta BPeraković DPeñalvo FCvitić I(2022)Spammer Detection Approaches in Online Social Network (OSNs): A SurveySustainable Management of Manufacturing Systems in Industry 4.010.1007/978-3-030-90462-3_11(159-180)Online publication date: 1-Feb-2022
https://doi.org/10.1007/978-3-030-90462-3_11
Indrasiri PHalgamuge MMohammad A(2021)Robust Ensemble Machine Learning Model for Filtering Phishing URLs: Expandable Random Gradient Stacked Voting Classifier (ERG-SVC)IEEE Access10.1109/ACCESS.2021.31246289(150142-150161)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3124628
Hai-Jew S(2020)Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL BasicRobotic Systems10.4018/978-1-7998-1754-3.ch060(1250-1289)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1754-3.ch060
Padmanabhan SMaramreddy PCyriac M(2020)Spam Detection in Link Shortening Web Services Through Social Network Data AnalysisData Engineering and Communication Technology10.1007/978-981-15-1097-7_9(103-118)Online publication date: 9-Jan-2020
https://doi.org/10.1007/978-981-15-1097-7_9
Yan HZhang XXie JHu C(2019)Detecting Malicious URLs Using a Deep Learning Approach Based on Stacked Denoising AutoencoderTrusted Computing and Information Security10.1007/978-981-13-5913-2_23(372-388)Online publication date: 9-Jan-2019
https://doi.org/10.1007/978-981-13-5913-2_23
Le Page SJourdan GBochmann GFlood JOnut I(2018)Using URL shorteners to compare phishing and malware attacks2018 APWG Symposium on Electronic Crime Research (eCrime)10.1109/ECRIME.2018.8376215(1-13)Online publication date: May-2018
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Choi DHan JChun SRappos ERobert SKwon T(2018)Bit.ly/practice: Uncovering content publishing and sharing through URL shortening servicesTelematics and Informatics10.1016/j.tele.2018.03.00335:5(1310-1323)Online publication date: Aug-2018
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Al-Janabi MQuincey EAndras P(2017)Using supervised machine learning algorithms to detect suspicious URLs in online social networksProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3116201(1104-1111)Online publication date: 31-Jul-2017
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