Detecting Pharmaceutical Spam in Microblog Messages

Detecting Pharmaceutical Spam in Microblog Messages

Kathy J. Liszka (University of Akron, USA), Chien-Chung Chan (University of Akron, USA) and Chandra Shekar (University of Akron, USA)
Copyright: © 2013 |Pages: 14
DOI: 10.4018/978-1-4666-2455-9.ch073

Abstract

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.
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Background

When email became a popular form of communication, junk mail flowing through the United States Postal Service morphed into spam messages flowing through Internet Service Providers. It is well known that spam is more than an annoyance dealt with over morning coffee. It consumes massive amounts of bandwidth, spreads malware, entices users to phishing sites, and offers products for sale that are either illegal or fake. We start by discussing Twitter as a medium for this undesirable activity and specific aspects that spammers use to mount successful campaigns. Then we focus on the pharmaceutical industry and the difference between traditional spam techniques and adaptations for intruding into the microblog world.

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