NDLI: Click traffic analysis of short URL spam on Twitter

Content Provider	IEEE Xplore Digital Library
Author	De Wang Navathe, S.B. Ling Liu Irani, D. Tamersoy, A. Pu, C.
Copyright Year	2013
Description	Author affiliation: Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA (De Wang; Navathe, S.B.; Ling Liu; Irani, D.; Tamersoy, A.; Pu, C.)
Abstract	With an average of 80% length reduction, the URL shorteners have become the norm for sharing URLs on Twitter, mainly due to the 140-character limit per message. Unfortunately, spammers have also adopted the URL shorteners to camouflage and improve the user click-through of their spam URLs. In this paper, we measure the misuse of the short URLs and analyze the characteristics of the spam and non-spam short URLs. We utilize these measurements to enable the detection of spam short URLs. To achieve this, we collected short URLs from Twitter and retrieved their click traffic data from Bitly, a popular URL shortening system. We first investigate the creators of over 600,000 Bitly short URLs to characterize short URL spammers. We then analyze the click traffic generated from various countries and referrers, and determine the top click sources for spam and non-spam short URLs. Our results show that the majority of the clicks are from direct sources and that the spammers utilize popular websites to attract more attention by cross-posting the links. We then use the click traffic data to classify the short URLs into spam vs. non-spam and compare the performance of the selected classifiers on the dataset. We determine that the Random Tree algorithm achieves the best performance with an accuracy of 90.81% and an F-measure value of 0.913.
Starting Page	250
Ending Page	259
File Size	642821
Page Count	10
File Format	PDF
ISBN	9781936968923
DOI	10.4108/icst.collaboratecom.2013.254084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	short URL spam Web pages Media Twitter traffic analysis Electronic mail Labeling Suspensions
Content Type	Text
Resource Type	Article

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