Searching for spam: Detecting fraudulent accounts via web search

Marcel Flores*, Aleksandar Kuzmanovic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Twitter users are harassed increasingly often by unsolicited messages that waste time and mislead users into clicking nefarious links. While increasingly powerful methods have been designed to detect spam, many depend on complex methods that require training and analyzing message content. While many of these systems are fast, implementing them in real time could present numerous challenges. Previous work has shown that large portions of spam originate from fraudulent accounts. We therefore propose a system which uses web searches to determine if a given account is fraudulent. The system uses the web searches to measure the online presence of a user and labels accounts with insufficient web presence to likely be fraudulent. Using our system on a collection of actual Twitter messages, we are able to achieve a true positive rate over 74% and a false positive rate below 11%, a detection rate comparable to those achieved by more expensive methods. Given its ability to operate before an account has produced a single tweet, we propose that our system could be used most effectively by combining it with slower more expensive machine learning methods as a first line of defense, alerting the system of fraudulent accounts before they have an opportunity to inject any spam into the ecosystem.

Original languageEnglish (US)
Title of host publicationPassive and Active Measurement - 14th International Conference, PAM 2013, Proceedings
PublisherSpringer Verlag
Pages208-217
Number of pages10
ISBN (Print)9783642365157
DOIs
StatePublished - 2013
Event14th International Conference on Passive and Active Measurement, PAM 2013 - Hong Kong, China
Duration: Mar 18 2013Mar 19 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7799 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Passive and Active Measurement, PAM 2013
Country/TerritoryChina
CityHong Kong
Period3/18/133/19/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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